diff --git a/additional_functions/check_loco_active.py b/additional_functions/check_loco_active.py new file mode 100644 index 000000000..97c044782 --- /dev/null +++ b/additional_functions/check_loco_active.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +""" +Check if the Unitree locomotion DDS domain / LocoClient is already initialized. +Does NOT send any commands — safe to run. +""" + +import sys +import psutil # pip install psutil if missing +import os + +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactory + +def is_loco_domain_active(): + """Detect if a ChannelFactory singleton is already initialized in this process.""" + fac = ChannelFactory() + active = getattr(fac, "_ChannelFactory__initialized", False) + participant = getattr(fac, "_ChannelFactory__participant", None) + domain = getattr(fac, "_ChannelFactory__domain", None) + return active, participant, domain + +def check_other_python_processes(): + """See if another Python process using the Unitree SDK is alive.""" + procs = [] + for p in psutil.process_iter(attrs=["pid", "name", "cmdline"]): + try: + if "python" in p.info["name"].lower() and any("unitree" in arg for arg in p.info["cmdline"]): + procs.append((p.info["pid"], " ".join(p.info["cmdline"]))) + except Exception: + pass + return procs + +if __name__ == "__main__": + active, participant, domain = is_loco_domain_active() + print("=== Local DDS Factory State ===") + print(f"Factory initialized: {active}") + print(f"Participant: {participant}") + print(f"Domain: {domain}") + print() + + print("=== Other Python processes using Unitree SDK ===") + procs = check_other_python_processes() + if not procs: + print("No other active Unitree-related Python processes found.") + else: + for pid, cmd in procs: + print(f"PID {pid}: {cmd}") + + print() + if os.path.exists("/tmp/CycloneDDS"): + print("Warning: /tmp/CycloneDDS exists — could indicate leftover DDS shared memory.") diff --git a/additional_functions/check_robot_connection.py b/additional_functions/check_robot_connection.py new file mode 100644 index 000000000..f21aebb02 --- /dev/null +++ b/additional_functions/check_robot_connection.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +""" +Check if robot is reachable and responding to DDS messages. +This helps diagnose the [ClientStub] send request error. +""" + +import time +import sys + +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient + +def check_connection(network_interface="en7"): + """Check connection to robot step by step.""" + print("=" * 60) + print("G1 Robot Connection Diagnostic") + print("=" * 60) + print(f"Network interface: {network_interface}") + print() + + # Step 1: Initialize DDS + print("[1/5] Initializing DDS ChannelFactory...") + try: + ChannelFactoryInitialize(0, network_interface) + print(" SUCCESS: ChannelFactory initialized") + except Exception as e: + print(f" FAILED: {e}") + return False + + # Step 2: Wait for discovery + print("[2/5] Waiting for DDS discovery (5 seconds)...") + print(" (This allows DDS to find the robot on the network)") + time.sleep(5) + print(" SUCCESS: Discovery period complete") + + # Step 3: Create LocoClient + print("[3/5] Creating LocoClient...") + try: + loco_client = LocoClient() + print(" SUCCESS: LocoClient created") + except Exception as e: + print(f" FAILED: {e}") + return False + + # Step 4: Set timeout and initialize + print("[4/5] Initializing LocoClient (timeout=10s)...") + try: + loco_client.SetTimeout(10.0) + loco_client.Init() + print(" SUCCESS: LocoClient initialized") + except Exception as e: + print(f" FAILED: {e}") + return False + + # Step 5: Test command + print("[5/5] Sending test command (ZeroTorque)...") + print(" This will show if robot is listening...") + try: + result = loco_client.ZeroTorque() + print(f" SUCCESS: Command sent, result: {result}") + print() + print("=" * 60) + print("CONNECTION TEST PASSED!") + print("=" * 60) + print("Your robot is connected and responding.") + print("The test_damp_zero_torque.py script should work now.") + return True + except Exception as e: + print(f" FAILED: {e}") + print() + print("=" * 60) + print("CONNECTION TEST FAILED") + print("=" * 60) + print() + print("Troubleshooting:") + print(" 1. Check robot is powered on") + print(" 2. Check network connection:") + print(f" sudo ifconfig {network_interface} 192.168.123.100 netmask 255.255.255.0") + print(" 3. Ping robot: ping 192.168.123.164") + print(" 4. Check robot mode (should be 'ai' mode)") + print(" 5. Check if another program is controlling the robot") + return False + +if __name__ == "__main__": + if len(sys.argv) > 1: + interface = sys.argv[1] + else: + interface = "en7" + + check_connection(interface) diff --git a/additional_functions/example_enhanced_sdk.py b/additional_functions/example_enhanced_sdk.py new file mode 100644 index 000000000..0536e78ad --- /dev/null +++ b/additional_functions/example_enhanced_sdk.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +""" +Simple example using the Enhanced LocoClient with cleanup. + +This demonstrates the enhanced SDK with proper resource management. +""" + +import sys +import time + +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.loco.g1_loco_client_enhanced import LocoClient + + +def main(): + network_interface = "en7" if len(sys.argv) < 2 else sys.argv[1] + + print("=" * 70) + print("G1 Enhanced SDK Example: Damp (1s) → Zero Torque (1s)") + print("=" * 70) + print(f"Network: {network_interface}\n") + + try: + # Initialize DDS + print("[1/4] Initializing DDS...") + ChannelFactoryInitialize(0, network_interface) + time.sleep(3) + print(" ✓ DDS ready\n") + + # Use context manager for automatic cleanup + print("[2/4] Connecting to robot...") + with LocoClient(auto_damp_on_close=True) as client: + client.SetTimeout(5.0) + print(" ✓ Connected\n") + + # Commands + print("[3/4] Executing commands...") + + print(" → DAMP mode...") + client.Damp() + time.sleep(1) + print(" ✓ Damp complete") + + print(" → ZERO TORQUE mode...") + client.ZeroTorque() + time.sleep(1) + print(" ✓ Zero torque complete\n") + + print("[4/4] Disconnecting...") + # Context manager will auto-cleanup and damp here + + print(" ✓ Disconnected safely\n") + print("=" * 70) + print("SUCCESS: All operations completed") + print("=" * 70) + + except KeyboardInterrupt: + print("\n⚠ Interrupted by user") + return 1 + + except Exception as e: + print(f"\n❌ Error: {e}") + return 1 + + return 0 + + +if __name__ == "__main__": + exit(main()) diff --git a/additional_functions/monitor_robot.py b/additional_functions/monitor_robot.py new file mode 100644 index 000000000..e06e16b4b --- /dev/null +++ b/additional_functions/monitor_robot.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +""" +G1 Robot Monitor - IMU, Force/Torque, Temperature, and Battery + +Displays: +- IMU data (orientation, gyro, accelerometer) +- Motor force/torque (estimated from current) +- Motor temperatures +- Battery status (requires BMS topic, may not be available) + +Usage: + python monitor_robot.py + Example: python monitor_robot.py en7 +""" + +import sys +import time +import numpy as np +from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_ + +# Try to import BMS (may not be available on all systems) +try: + from unitree_sdk2py.idl.unitree_hg.msg.dds_ import BmsState_ + HAS_BMS = True +except: + HAS_BMS = False + print("Note: BMS not available in this SDK version") + + +class RobotMonitor: + def __init__(self): + self.latest_state = None + self.latest_bms = None + self.update_count = 0 + + def state_handler(self, msg: LowState_): + """Handle low-level state updates (IMU, motors)""" + self.latest_state = msg + self.update_count += 1 + + def bms_handler(self, msg): + """Handle battery management system updates""" + self.latest_bms = msg + + def print_status(self): + """Print formatted sensor data""" + if not self.latest_state: + print("Waiting for robot data...") + return + + state = self.latest_state + + # Clear screen and print header + print("\033[2J\033[H") # Clear screen + print("=" * 80) + print(f"G1 ROBOT MONITOR - Updates: {self.update_count}") + print("=" * 80) + + # ============ IMU DATA ============ + print("\n🧭 IMU DATA:") + imu = state.imu_state + + # Orientation (Euler angles) + roll_deg = np.degrees(imu.rpy[0]) + pitch_deg = np.degrees(imu.rpy[1]) + yaw_deg = np.degrees(imu.rpy[2]) + print(f" Orientation:") + print(f" Roll: {roll_deg:+7.2f}°") + print(f" Pitch: {pitch_deg:+7.2f}°") + print(f" Yaw: {yaw_deg:+7.2f}°") + + # Angular velocity (gyroscope) + print(f" Angular Velocity (rad/s):") + print(f" X: {imu.gyroscope[0]:+7.3f}") + print(f" Y: {imu.gyroscope[1]:+7.3f}") + print(f" Z: {imu.gyroscope[2]:+7.3f}") + + # Linear acceleration + print(f" Linear Acceleration (m/s²):") + print(f" X: {imu.accelerometer[0]:+7.3f}") + print(f" Y: {imu.accelerometer[1]:+7.3f}") + print(f" Z: {imu.accelerometer[2]:+7.3f}") + + # Gravity magnitude (should be ~9.81 when stationary) + accel_mag = np.linalg.norm(imu.accelerometer) + print(f" Acceleration Magnitude: {accel_mag:.2f} m/s² (gravity ~9.81)") + + print(f" IMU Temperature: {imu.temperature}°C") + + # ============ ALL MOTORS: TORQUE & TEMPERATURE ============ + print("\n⚡ ALL MOTORS - TORQUE & TEMPERATURE:") + print(f" {'Motor':<8} {'Torque (Nm)':<14} {'Temp (°C)':<12} {'Status'}") + print(f" {'-'*8} {'-'*14} {'-'*12} {'-'*20}") + + for i in range(35): + motor = state.motor_state[i] + torque = motor.tau_est + temp = motor.temperature[0] + + # Status indicators + status = [] + if abs(torque) > 5.0: + status.append("⚡HIGH TORQUE") + if temp > 60: + status.append("🔥HOT") + elif temp > 50: + status.append("⚠️ WARM") + + status_str = " ".join(status) if status else "✓" + + print(f" {i:2d} {torque:+7.3f} {temp:3d} {status_str}") + + # Summary statistics + torques = [state.motor_state[i].tau_est for i in range(35)] + temps = [state.motor_state[i].temperature[0] for i in range(35)] + + print(f"\n Statistics:") + print(f" Torque: min={min([abs(t) for t in torques]):.2f} " + f"max={max([abs(t) for t in torques]):.2f} " + f"mean={np.mean([abs(t) for t in torques]):.2f} Nm") + print(f" Temp: min={min(temps)} max={max(temps)} mean={np.mean(temps):.1f}°C") + + # ============ BATTERY ============ + print("\n🔋 BATTERY:") + if self.latest_bms: + bms = self.latest_bms + print(f" State of Charge: {bms.soc}%") + print(f" State of Health: {bms.soh}%") + print(f" Voltage: {bms.bmsvoltage[0] / 1000.0:.2f} V") + print(f" Current: {bms.current / 1000.0:.2f} A") + + # Power calculation + power = (bms.bmsvoltage[0] / 1000.0) * (bms.current / 1000.0) + print(f" Power: {power:.2f} W") + + # Temperature range + temps = [t / 10.0 for t in bms.temperature] + print(f" Battery Temp: {min(temps):.1f}°C to {max(temps):.1f}°C") + print(f" Charge Cycles: {bms.cycle}") + else: + print(f" Battery data not available") + print(f" (BMS topic may not be published or accessible)") + + # ============ SYSTEM INFO ============ + print(f"\n⚙️ SYSTEM:") + print(f" Tick: {state.tick}") + print(f" Mode: machine={state.mode_machine}, pr={state.mode_pr}") + + print("\n" + "=" * 80) + print("Press Ctrl+C to stop") + + +def main(): + if len(sys.argv) < 2: + print(f"Usage: python3 {sys.argv[0]} ") + print(f"Example: python3 {sys.argv[0]} en7") + sys.exit(1) + + network_interface = sys.argv[1] + + print("=" * 80) + print("G1 ROBOT MONITOR") + print("=" * 80) + print(f"Initializing DDS on {network_interface}...") + ChannelFactoryInitialize(0, network_interface) + + # Create monitor + monitor = RobotMonitor() + + # Subscribe to low-level state (IMU, motors) + print("Subscribing to rt/lowstate...") + lowstate_sub = ChannelSubscriber("rt/lowstate", LowState_) + lowstate_sub.Init(monitor.state_handler, 10) + + # Try to subscribe to BMS (may not be available) + if HAS_BMS: + print("Attempting to subscribe to battery data...") + try: + # Common BMS topic names + for topic in ["rt/bms", "rt/battery", "rt/bms_state"]: + try: + bms_sub = ChannelSubscriber(topic, BmsState_) + bms_sub.Init(monitor.bms_handler, 10) + print(f" Subscribed to {topic}") + break + except: + pass + except Exception as e: + print(f" Battery data not available: {e}") + + print("\nWaiting for data...") + time.sleep(1) + + print("Starting monitor...\n") + + try: + while True: + monitor.print_status() + time.sleep(0.5) # Update display at 2 Hz + except KeyboardInterrupt: + print("\n\n" + "=" * 80) + print("Monitor stopped") + print("=" * 80) + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/read_joint_positions.py b/additional_functions/read_joint_positions.py new file mode 100644 index 000000000..c4042bf6d --- /dev/null +++ b/additional_functions/read_joint_positions.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +""" +G1 Robot Joint Position Reader + +Displays real-time joint positions, velocities, and accelerations for all 35 motors. + +Usage: + python read_joint_positions.py + Example: python read_joint_positions.py en7 +""" + +import sys +import time +import numpy as np +from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_ + + +# G1 Joint Names (35 motors) +JOINT_NAMES = { + # Legs (0-11) + 0: "left_hip_pitch", + 1: "left_hip_roll", + 2: "left_hip_yaw", + 3: "left_knee", + 4: "left_ankle_pitch", + 5: "left_ankle_roll", + 6: "right_hip_pitch", + 7: "right_hip_roll", + 8: "right_hip_yaw", + 9: "right_knee", + 10: "right_ankle_pitch", + 11: "right_ankle_roll", + + # Waist (12-14) + 12: "waist_yaw", + 13: "waist_roll", + 14: "waist_pitch", + + # Head (15-16) + 15: "head_yaw", + 16: "head_pitch", + + # Left Arm (17-23) + 17: "left_shoulder_pitch", + 18: "left_shoulder_roll", + 19: "left_shoulder_yaw", + 20: "left_elbow_pitch", + 21: "left_elbow_roll", + 22: "left_wrist_yaw", + 23: "left_wrist_pitch", + + # Right Arm (24-30) + 24: "right_shoulder_pitch", + 25: "right_shoulder_roll", + 26: "right_shoulder_yaw", + 27: "right_elbow_pitch", + 28: "right_elbow_roll", + 29: "right_wrist_yaw", + 30: "right_wrist_pitch", + + # Hands (31-34) - if applicable + 31: "left_hand", + 32: "right_hand", + 33: "reserved_33", + 34: "reserved_34", +} + + +class JointReader: + def __init__(self): + self.latest_state = None + self.update_count = 0 + + def state_handler(self, msg: LowState_): + """Handle low-level state updates""" + self.latest_state = msg + self.update_count += 1 + + def print_positions(self, mode='all'): + """ + Print joint positions + mode: 'all', 'legs', 'arms', 'compact' + """ + if not self.latest_state: + print("Waiting for robot data...") + return + + state = self.latest_state + + # Clear screen and print header + print("\033[2J\033[H") # Clear screen + print("=" * 100) + print(f"G1 JOINT POSITIONS - Updates: {self.update_count}") + print("=" * 100) + + if mode == 'compact': + self._print_compact(state) + else: + self._print_detailed(state, mode) + + print("\n" + "=" * 100) + print("Press Ctrl+C to stop") + + def _print_detailed(self, state, mode): + """Print detailed joint information""" + print(f"\n{'ID':<4} {'Joint Name':<25} {'Position (rad)':<16} {'Position (deg)':<16} {'Velocity (rad/s)':<18} {'Accel (rad/s²)'}") + print("-" * 100) + + for i in range(35): + # Filter by mode + if mode == 'legs' and i >= 12: + continue + elif mode == 'arms' and not (17 <= i <= 30): + continue + + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + + position_rad = motor.q + position_deg = np.degrees(position_rad) + velocity = motor.dq + acceleration = motor.ddq + + print(f"{i:<4} {joint_name:<25} {position_rad:+8.4f} {position_deg:+8.2f}° " + f"{velocity:+8.4f} {acceleration:+8.4f}") + + def _print_compact(self, state): + """Print compact view - positions only""" + print("\n🦿 LEGS:") + for i in range(12): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + print("\n🦴 WAIST:") + for i in range(12, 15): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + print("\n🗣️ HEAD:") + for i in range(15, 17): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + print("\n🦾 LEFT ARM:") + for i in range(17, 24): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + print("\n🦾 RIGHT ARM:") + for i in range(24, 31): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + print("\n👋 HANDS:") + for i in range(31, 35): + motor = state.motor_state[i] + joint_name = JOINT_NAMES.get(i, f"motor_{i}") + print(f" [{i:2d}] {joint_name:<20} {motor.q:+7.4f} rad ({np.degrees(motor.q):+7.2f}°)") + + +def main(): + if len(sys.argv) < 2: + print(f"Usage: python3 {sys.argv[0]} [mode]") + print(f"Example: python3 {sys.argv[0]} en7 compact") + print(f"\nModes:") + print(f" all - Show all joints with full details (default)") + print(f" compact - Organized by body part") + print(f" legs - Only leg joints") + print(f" arms - Only arm joints") + sys.exit(1) + + network_interface = sys.argv[1] + mode = sys.argv[2] if len(sys.argv) > 2 else 'compact' + + print("=" * 100) + print("G1 JOINT POSITION READER") + print("=" * 100) + print(f"Initializing DDS on {network_interface}...") + ChannelFactoryInitialize(0, network_interface) + + # Create reader + reader = JointReader() + + # Subscribe to low-level state + print("Subscribing to rt/lowstate...") + lowstate_sub = ChannelSubscriber("rt/lowstate", LowState_) + lowstate_sub.Init(reader.state_handler, 10) + + print("\nWaiting for data...") + time.sleep(1) + + print(f"Starting monitor (mode: {mode})...\n") + + try: + while True: + reader.print_positions(mode) + time.sleep(0.1) # Update display at 10 Hz + except KeyboardInterrupt: + print("\n\n" + "=" * 100) + print("Reader stopped") + print("=" * 100) + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/sniff.py b/additional_functions/sniff.py new file mode 100644 index 000000000..335fde1d2 --- /dev/null +++ b/additional_functions/sniff.py @@ -0,0 +1,58 @@ +import time, threading, numpy as np +from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as HGLowCmd + +TOPIC = "rt/lowcmd" +N = 35 + +class LowCmdSniffer: + def __init__(self, iface="en7"): + ChannelFactoryInitialize(0, iface) + self.sub = ChannelSubscriber(TOPIC, HGLowCmd) + # Use a queue depth; passing None as handler is fine for polling + self.sub.Init(None, 20) + + self._lock = threading.Lock() + self._have = False + self.q = np.zeros(N) + self.dq = np.zeros(N) + self.kp = np.zeros(N) + self.kd = np.zeros(N) + self.tau = np.zeros(N) + + threading.Thread(target=self._loop, daemon=True).start() + + def _loop(self): + while True: + msg = self.sub.Read() + if msg is not None: + with self._lock: + for i in range(N): + mc = msg.motor_cmd[i] + self.q[i] = getattr(mc, "q", 0.0) + self.dq[i] = getattr(mc, "qd", 0.0) # named qd in HG + self.kp[i] = getattr(mc, "kp", 0.0) + self.kd[i] = getattr(mc, "kd", 0.0) + self.tau[i] = getattr(mc, "tau", 0.0) + self._have = True + time.sleep(0.001) + + def latest(self): + with self._lock: + return self._have, self.q.copy(), self.dq.copy(), self.kp.copy(), self.kd.copy(), self.tau.copy() + +if __name__ == "__main__": + sniffer = LowCmdSniffer(iface="en7") + t0 = time.time() + while time.time() - t0 < 5: + ok, q, dq, kp, kd, tau = sniffer.latest() + if ok: + print("kp :", kp.round(2)) + print("kd :", kd.round(2)) + print("q :", q.round(4)) + print("dq :", dq.round(4)) + print("tau:", tau.round(3)) + break + time.sleep(0.01) + else: + print("No LowCmd messages seen on rt/lowcmd in 5s (check topic/type/interface).") diff --git a/additional_functions/snifflonger.py b/additional_functions/snifflonger.py new file mode 100644 index 000000000..cc0c17cc7 --- /dev/null +++ b/additional_functions/snifflonger.py @@ -0,0 +1,32 @@ +# snifflonger.py +import csv, time, sys +from sniff import LowCmdSniffer # uses latest() -> ok, q, dq, kp, kd, tau + +IFACE = sys.argv[1] if len(sys.argv) > 1 else "en7" +DT = float(sys.argv[2]) if len(sys.argv) > 2 else 0.01 # seconds + +N = 35 + +def header(prefix): + return [f"{prefix}{i}" for i in range(N)] + +def main(): + sniffer = LowCmdSniffer(iface=IFACE) + + with open("lowcmd_log.csv", "w", newline="") as f: + w = csv.writer(f) + w.writerow(["t"] + header("kp") + header("kd") + header("q") + header("dq") + header("tau")) + try: + while True: + ok, q, dq, kp, kd, tau = sniffer.latest() + if ok: + t = time.time() + # ensure lists (in case numpy arrays) + w.writerow([t, *kp.tolist(), *kd.tolist(), *q.tolist(), *dq.tolist(), *tau.tolist()]) + f.flush() + time.sleep(DT) + except KeyboardInterrupt: + pass + +if __name__ == "__main__": + main() diff --git a/additional_functions/test.py b/additional_functions/test.py new file mode 100644 index 000000000..34e5fbd3b --- /dev/null +++ b/additional_functions/test.py @@ -0,0 +1,36 @@ +from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1, G1_29_JointIndex +from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +import time +config = UnitreeG1Config( + motion_mode=False, + simulation_mode=False +) + +robot = UnitreeG1(config) +#print(robot.get_observation()) +#robot.calibrate() +#print(robot.get_observation()) +while True: + observation = robot.get_observation() + robot.send_action(observation) + #print(robot.get_observation()) + time.sleep(0.01) +# print(observation) +# time.sleep(0.1) + +# dataset = LeRobotDataset(repo_id='nepyope/unitree_box_push_30fps_actions_fix', root="", episodes=[0]) + +# actions = dataset.hf_dataset.select_columns("action") +# print(actions) +# episode_idx = 0 +# episode_info = dataset.meta.episodes[episode_idx] + +# from_idx = episode_info["dataset_from_index"] +# to_idx = episode_info["dataset_to_index"] + + +# for idx in range(from_idx, to_idx): +# robot.send_action(actions[idx]["action"].numpy()[:14]) +# time.sleep(1.0 / 30) diff --git a/additional_functions/test_damp_zero_torque.py b/additional_functions/test_damp_zero_torque.py new file mode 100644 index 000000000..84c21a77b --- /dev/null +++ b/additional_functions/test_damp_zero_torque.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +""" +Test script to switch between damp and zero torque mode every 2 seconds. +This script demonstrates switching between the safest robot modes. +""" + +import time +import sys +import signal + +# Add the unitree_sdk2_python path to sys.path +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient + + +class DampZeroTorqueTester: + def __init__(self): + # Initialize locomotion client directly (like the working examples) + self.loco_client = LocoClient() + self.loco_client.SetTimeout(10.0) # Increased timeout to allow robot to connect + self.loco_client.Init() + self.running = True + + # Set up signal handler for graceful shutdown + signal.signal(signal.SIGINT, self.signal_handler) + + def signal_handler(self, sig, frame): + """Handle Ctrl+C gracefully.""" + print("\nReceived interrupt signal. Shutting down...") + self.running = False + + def switch_to_damp(self): + """Switch robot to damping mode.""" + try: + print("[Command] Switching to DAMP mode (motors off)...") + result = self.loco_client.Damp() + print(f"[Result] Damp command result: {result}") + return True + except Exception as e: + print(f"[Error] Failed to switch to damp mode: {e}") + return False + + def switch_to_zero_torque(self): + """Switch robot to zero torque mode.""" + try: + print("[Command] Switching to ZERO TORQUE mode (motors on, no torque)...") + result = self.loco_client.ZeroTorque() + print(f"[Result] ZeroTorque command result: {result}") + return True + except Exception as e: + print(f"[Error] Failed to switch to zero torque mode: {e}") + return False + + def run_test(self): + """Run exactly 2 cycles: damp → zero_torque → damp → zero_torque.""" + print("=" * 60) + print("G1 Robot Damp/Zero Torque Mode Switcher") + print("=" * 60) + print("Running exactly 2 cycles:") + print(" Cycle 1: DAMP → ZERO TORQUE") + print(" Cycle 2: ZERO TORQUE → DAMP → ZERO TORQUE") + print(" Final mode: ZERO TORQUE") + print("\nWARNING: Ensure robot is in a safe position!") + print("Press Ctrl+C to stop safely") + print("=" * 60) + + # Wait for user confirmation + input("Press Enter to start the test...") + + start_time = time.time() + + # Initial setup - start in damp mode + print(f"\nStarting test...") + if not self.switch_to_damp(): + print("Failed to initialize damp mode. Aborting.") + return + + # Cycle 1: DAMP → ZERO TORQUE + print(f"\nCycle 1: DAMP → ZERO TORQUE") + print("Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_zero_torque(): + print("Failed to switch to zero torque in cycle 1.") + return + + # Cycle 2: ZERO TORQUE → DAMP → ZERO TORQUE + + print(f"\nCycle 2: ZERO TORQUE → DAMP → ZERO TORQUE") + print("Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_damp(): + print("Failed to switch to damp in cycle 2.") + return + + print("Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_zero_torque(): + print("Failed to switch to zero torque in cycle 2.") + return + + # Test completed - robot ends in zero torque mode + print(f"\nTest completed after 2 cycles") + print("Final mode: ZERO TORQUE") + + print("\nTest finished!") + print(f"Total cycles: 2") + print(f"Total time: {time.time() - start_time:.1f} seconds") + + +def main(): + print("G1 Robot Damp/Zero Torque Mode Switcher") + print("This script will run exactly 2 cycles ending in zero torque mode.") + + # Get network interface from command line or use default + if len(sys.argv) > 1: + network_interface = sys.argv[1] + print(f"Using network interface: {network_interface}") + else: + network_interface = "en7" # Default based on your setup + print(f"Using default network interface: {network_interface}") + + print("\nInitializing connection...") + + tester = None + try: + # Initialize the channel factory + ChannelFactoryInitialize(0, network_interface) + print("Channel factory initialized") + + # Wait for DDS to discover robot (CRITICAL!) + print("Waiting for DDS discovery (3 seconds)...") + time.sleep(3) + print("Discovery complete") + + # Create tester + tester = DampZeroTorqueTester() + print("LocoClient created and ready") + + # Run the test + tester.run_test() + del tester #calls close() + + except Exception as e: + print(f"Error during initialization: {e}") + print("\nTroubleshooting:") + print(" - Make sure you're connected to the robot's network") + print(" - Try: sudo ifconfig en7 192.168.123.100 netmask 255.255.255.0") + print(" - Verify robot is powered on and accessible") + print(" - Run check_robot_connection.py for detailed diagnostics") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/additional_functions/test_dummy_actions.py b/additional_functions/test_dummy_actions.py new file mode 100644 index 000000000..3ba5eb7e8 --- /dev/null +++ b/additional_functions/test_dummy_actions.py @@ -0,0 +1,263 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Test script with dummy actions to isolate robot communication issues. +Sends constant 0.5 values for all actions without reading teleoperator. + +Example: + +```shell +python test_dummy_actions.py \ + --robot.type=unitree_g1 \ + --teleop.type=custom \ + --fps=60 \ + --duration=10.0 +``` + +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pprint import pformat + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 +from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig # noqa: F401 +from lerobot.configs import parser +from lerobot.processor import ( + make_default_processors, +) +from lerobot.robots import ( # noqa: F401 + Robot, + RobotConfig, + bi_so100_follower, + hope_jr, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, + unitree_g1, +) +from lerobot.teleoperators import ( # noqa: F401 + Teleoperator, + TeleoperatorConfig, + bi_so100_leader, + gamepad, + homunculus, + koch_leader, + make_teleoperator_from_config, + so100_leader, + so101_leader, + custom +) +from lerobot.utils.import_utils import register_third_party_devices +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import init_logging, move_cursor_up + + +@dataclass +class TestDummyConfig: + teleop: TeleoperatorConfig + robot: RobotConfig + # Limit the maximum frames per second. + fps: int = 60 + # Test duration in seconds + duration: float = 10.0 + + +def test_dummy_loop( + teleop: Teleoperator, + robot: Robot, + fps: int, + duration: float, +): + """ + Test loop that sends dummy actions (all 0.5) to the robot. + + Args: + teleop: The teleoperator device instance (used only to get action features). + robot: The robot instance. + fps: The target frequency for the control loop in frames per second. + duration: The duration of the test in seconds. + """ + + teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() + + # Create dummy action with all values set to 0.5 + dummy_action = {key: 0.5 for key in teleop.action_features.keys()} + + print(f"\nDummy action (all 0.5):") + for key in list(dummy_action.keys())[:5]: + print(f" {key}: {dummy_action[key]}") + if len(dummy_action) > 5: + print(f" ... and {len(dummy_action) - 5} more") + + start = time.perf_counter() + iteration = 0 + + # Timing accumulators + total_obs_time = 0 + total_process_time = 0 + total_robot_send_time = 0 + + min_obs_time = float('inf') + max_obs_time = 0 + min_process_time = float('inf') + max_process_time = 0 + min_robot_send_time = float('inf') + max_robot_send_time = 0 + + print(f"\nStarting dummy action test for {duration}s at {fps} FPS target...") + print(f"Robot: {robot.__class__.__name__}") + print(f"Sending constant actions: all values = 0.5") + print("-" * 80) + + while True: + loop_start = time.perf_counter() + + # 1. Get robot observation + t1 = time.perf_counter() + obs = robot.get_observation() + obs_time = time.perf_counter() - t1 + + # 2. Use dummy action (no teleop read) + raw_action = dummy_action + + # 3. Process action through pipeline + t3 = time.perf_counter() + teleop_action = teleop_action_processor((raw_action, obs)) + robot_action_to_send = robot_action_processor((teleop_action, obs)) + process_time = time.perf_counter() - t3 + + # 4. Send action to robot + t5 = time.perf_counter() + result = robot.send_action(robot_action_to_send) + robot_send_time = time.perf_counter() - t5 + + # Track statistics + total_obs_time += obs_time + total_process_time += process_time + total_robot_send_time += robot_send_time + + min_obs_time = min(min_obs_time, obs_time) + max_obs_time = max(max_obs_time, obs_time) + min_process_time = min(min_process_time, process_time) + max_process_time = max(max_process_time, process_time) + min_robot_send_time = min(min_robot_send_time, robot_send_time) + max_robot_send_time = max(max_robot_send_time, robot_send_time) + + iteration += 1 + + # Display timing breakdown + total_component_time = obs_time + process_time + robot_send_time + + print(f"\nIteration: {iteration}") + print(f"1. robot.get_observation(): {obs_time * 1e3:>7.2f}ms") + print(f"2. action processing: {process_time * 1e3:>7.2f}ms") + print(f"3. robot.send_action(): {robot_send_time * 1e3:>7.2f}ms") + print(f" Total component time: {total_component_time * 1e3:>7.2f}ms") + print(f" send_action() result: {result}") + + # Show sent values + print(f"\nSent actions:") + print(f" kLeftWristRoll.pos: {robot_action_to_send.get('kLeftWristRoll.pos', 'N/A')}") + print(f" kRightWristRoll.pos: {robot_action_to_send.get('kRightWristRoll.pos', 'N/A')}") + + move_cursor_up(11) + + # Wait to maintain target FPS + dt_s = time.perf_counter() - loop_start + busy_wait(1 / fps - dt_s) + + loop_time = time.perf_counter() - loop_start + + # Check if duration reached + elapsed = time.perf_counter() - start + if elapsed >= duration: + break + + # Print final statistics + print("\n" + "=" * 80) + print("FINAL TIMING BREAKDOWN (DUMMY ACTIONS)") + print("=" * 80) + print(f"Total iterations: {iteration}") + print(f"Total time: {elapsed:.2f}s") + print(f"Actual FPS: {iteration / elapsed:.2f}") + print(f"Target FPS: {fps}") + print("\n" + "-" * 80) + print(f"{'Component':<35} {'Avg (ms)':>10} {'Min (ms)':>10} {'Max (ms)':>10} {'% of time':>10}") + print("-" * 80) + + avg_obs = total_obs_time / iteration * 1e3 + avg_process = total_process_time / iteration * 1e3 + avg_robot_send = total_robot_send_time / iteration * 1e3 + + total_avg = avg_obs + avg_process + avg_robot_send + + print(f"{'1. robot.get_observation()':<35} {avg_obs:>10.2f} {min_obs_time*1e3:>10.2f} {max_obs_time*1e3:>10.2f} {avg_obs/total_avg*100:>9.1f}%") + print(f"{'2. action processing':<35} {avg_process:>10.2f} {min_process_time*1e3:>10.2f} {max_process_time*1e3:>10.2f} {avg_process/total_avg*100:>9.1f}%") + print(f"{'3. robot.send_action()':<35} {avg_robot_send:>10.2f} {min_robot_send_time*1e3:>10.2f} {max_robot_send_time*1e3:>10.2f} {avg_robot_send/total_avg*100:>9.1f}%") + print("-" * 80) + print(f"{'TOTAL':<35} {total_avg:>10.2f}") + print("=" * 80) + + +@parser.wrap() +def test_dummy(cfg: TestDummyConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + print("\nInitializing teleoperator (for action features only)...") + teleop = make_teleoperator_from_config(cfg.teleop) + + print("Initializing robot...") + robot = make_robot_from_config(cfg.robot) + + print("Connecting teleoperator...") + teleop.connect() + + print("Connecting robot...") + robot.connect() + + print(f"\nTeleoperator connected: {teleop.is_connected}") + print(f"Robot connected: {robot.is_connected}") + print(f"Action features count: {len(teleop.action_features)}") + + try: + test_dummy_loop( + teleop=teleop, + robot=robot, + fps=cfg.fps, + duration=cfg.duration, + ) + except KeyboardInterrupt: + print("\nTest interrupted by user") + finally: + print("\nDisconnecting...") + teleop.disconnect() + robot.disconnect() + + +def main(): + register_third_party_devices() + test_dummy() + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_full_pipeline.py b/additional_functions/test_full_pipeline.py new file mode 100644 index 000000000..d2e585738 --- /dev/null +++ b/additional_functions/test_full_pipeline.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Test script to measure each component of the teleoperation pipeline separately. + +Example: + +```shell +python test_full_pipeline.py \ + --robot.type=unitree_g1 \ + --teleop.type=custom \ + --fps=60 \ + --duration=10.0 +``` + +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pprint import pformat + +from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig # noqa: F401 +from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig # noqa: F401 +from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig # noqa: F401 +from lerobot.configs import parser +from lerobot.processor import ( + make_default_processors, +) +from lerobot.robots import ( # noqa: F401 + Robot, + RobotConfig, + bi_so100_follower, + hope_jr, + koch_follower, + make_robot_from_config, + so100_follower, + so101_follower, + unitree_g1, +) +from lerobot.teleoperators import ( # noqa: F401 + Teleoperator, + TeleoperatorConfig, + bi_so100_leader, + gamepad, + homunculus, + koch_leader, + make_teleoperator_from_config, + so100_leader, + so101_leader, + custom +) +from lerobot.utils.import_utils import register_third_party_devices +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import init_logging, move_cursor_up + + +@dataclass +class TestPipelineConfig: + teleop: TeleoperatorConfig + robot: RobotConfig + # Limit the maximum frames per second. + fps: int = 60 + # Test duration in seconds + duration: float = 10.0 + + +def test_pipeline_loop( + teleop: Teleoperator, + robot: Robot, + fps: int, + duration: float, +): + """ + Test loop that measures timing of each pipeline component separately. + + Args: + teleop: The teleoperator device instance. + robot: The robot instance. + fps: The target frequency for the control loop in frames per second. + duration: The duration of the test in seconds. + """ + + teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors() + + start = time.perf_counter() + iteration = 0 + + # Timing accumulators + total_obs_time = 0 + total_teleop_read_time = 0 + total_teleop_process_time = 0 + total_robot_process_time = 0 + total_robot_send_time = 0 + + min_obs_time = float('inf') + max_obs_time = 0 + min_teleop_read_time = float('inf') + max_teleop_read_time = 0 + min_teleop_process_time = float('inf') + max_teleop_process_time = 0 + min_robot_process_time = float('inf') + max_robot_process_time = 0 + min_robot_send_time = float('inf') + max_robot_send_time = 0 + + print(f"\nStarting full pipeline test for {duration}s at {fps} FPS target...") + print(f"Robot: {robot.__class__.__name__}") + print(f"Teleoperator: {teleop.__class__.__name__}") + print("-" * 80) + + while True: + loop_start = time.perf_counter() + + # 1. Get robot observation + t1 = time.perf_counter() + obs = robot.get_observation() + obs_time = time.perf_counter() - t1 + + # 2. Get teleop action + t2 = time.perf_counter() + raw_action = teleop.get_action() + teleop_read_time = time.perf_counter() - t2 + + # 3. Process teleop action + t3 = time.perf_counter() + teleop_action = teleop_action_processor((raw_action, obs)) + teleop_process_time = time.perf_counter() - t3 + + # 4. Process action for robot + t4 = time.perf_counter() + robot_action_to_send = robot_action_processor((teleop_action, obs)) + robot_process_time = time.perf_counter() - t4 + + # 5. Send action to robot + t5 = time.perf_counter() + _ = robot.send_action(robot_action_to_send) + robot_send_time = time.perf_counter() - t5 + + # Track statistics + total_obs_time += obs_time + total_teleop_read_time += teleop_read_time + total_teleop_process_time += teleop_process_time + total_robot_process_time += robot_process_time + total_robot_send_time += robot_send_time + + min_obs_time = min(min_obs_time, obs_time) + max_obs_time = max(max_obs_time, obs_time) + min_teleop_read_time = min(min_teleop_read_time, teleop_read_time) + max_teleop_read_time = max(max_teleop_read_time, teleop_read_time) + min_teleop_process_time = min(min_teleop_process_time, teleop_process_time) + max_teleop_process_time = max(max_teleop_process_time, teleop_process_time) + min_robot_process_time = min(min_robot_process_time, robot_process_time) + max_robot_process_time = max(max_robot_process_time, robot_process_time) + min_robot_send_time = min(min_robot_send_time, robot_send_time) + max_robot_send_time = max(max_robot_send_time, robot_send_time) + + iteration += 1 + + # Display timing breakdown + total_component_time = obs_time + teleop_read_time + teleop_process_time + robot_process_time + robot_send_time + + print(f"\nIteration: {iteration}") + print(f"1. robot.get_observation(): {obs_time * 1e3:>7.2f}ms") + print(f"2. teleop.get_action(): {teleop_read_time * 1e3:>7.2f}ms") + print(f"3. teleop_action_processor: {teleop_process_time * 1e3:>7.2f}ms") + print(f"4. robot_action_processor: {robot_process_time * 1e3:>7.2f}ms") + print(f"5. robot.send_action(): {robot_send_time * 1e3:>7.2f}ms") + print(f" Total component time: {total_component_time * 1e3:>7.2f}ms") + + # Show wrist roll values + print(f"\nkLeftWristRoll.pos: {robot_action_to_send.get('kLeftWristRoll.pos', 'N/A')}") + print(f"kRightWristRoll.pos: {robot_action_to_send.get('kRightWristRoll.pos', 'N/A')}") + + move_cursor_up(11) + + # Wait to maintain target FPS + dt_s = time.perf_counter() - loop_start + busy_wait(1 / fps - dt_s) + + loop_time = time.perf_counter() - loop_start + + # Check if duration reached + elapsed = time.perf_counter() - start + if elapsed >= duration: + break + + # Print final statistics + print("\n" + "=" * 80) + print("FINAL TIMING BREAKDOWN") + print("=" * 80) + print(f"Total iterations: {iteration}") + print(f"Total time: {elapsed:.2f}s") + print(f"Actual FPS: {iteration / elapsed:.2f}") + print(f"Target FPS: {fps}") + print("\n" + "-" * 80) + print(f"{'Component':<35} {'Avg (ms)':>10} {'Min (ms)':>10} {'Max (ms)':>10} {'% of time':>10}") + print("-" * 80) + + avg_obs = total_obs_time / iteration * 1e3 + avg_teleop_read = total_teleop_read_time / iteration * 1e3 + avg_teleop_proc = total_teleop_process_time / iteration * 1e3 + avg_robot_proc = total_robot_process_time / iteration * 1e3 + avg_robot_send = total_robot_send_time / iteration * 1e3 + + total_avg = avg_obs + avg_teleop_read + avg_teleop_proc + avg_robot_proc + avg_robot_send + + print(f"{'1. robot.get_observation()':<35} {avg_obs:>10.2f} {min_obs_time*1e3:>10.2f} {max_obs_time*1e3:>10.2f} {avg_obs/total_avg*100:>9.1f}%") + print(f"{'2. teleop.get_action()':<35} {avg_teleop_read:>10.2f} {min_teleop_read_time*1e3:>10.2f} {max_teleop_read_time*1e3:>10.2f} {avg_teleop_read/total_avg*100:>9.1f}%") + print(f"{'3. teleop_action_processor':<35} {avg_teleop_proc:>10.2f} {min_teleop_process_time*1e3:>10.2f} {max_teleop_process_time*1e3:>10.2f} {avg_teleop_proc/total_avg*100:>9.1f}%") + print(f"{'4. robot_action_processor':<35} {avg_robot_proc:>10.2f} {min_robot_process_time*1e3:>10.2f} {max_robot_process_time*1e3:>10.2f} {avg_robot_proc/total_avg*100:>9.1f}%") + print(f"{'5. robot.send_action()':<35} {avg_robot_send:>10.2f} {min_robot_send_time*1e3:>10.2f} {max_robot_send_time*1e3:>10.2f} {avg_robot_send/total_avg*100:>9.1f}%") + print("-" * 80) + print(f"{'TOTAL':<35} {total_avg:>10.2f}") + print("=" * 80) + + +@parser.wrap() +def test_pipeline(cfg: TestPipelineConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + print("\nInitializing teleoperator...") + teleop = make_teleoperator_from_config(cfg.teleop) + + print("Initializing robot...") + robot = make_robot_from_config(cfg.robot) + + print("Connecting teleoperator...") + teleop.connect() + + print("Connecting robot...") + robot.connect() + + print(f"\nTeleoperator connected: {teleop.is_connected}") + print(f"Teleoperator calibrated: {teleop.is_calibrated}") + print(f"Robot connected: {robot.is_connected}") + + try: + test_pipeline_loop( + teleop=teleop, + robot=robot, + fps=cfg.fps, + duration=cfg.duration, + ) + except KeyboardInterrupt: + print("\nTest interrupted by user") + finally: + print("\nDisconnecting...") + teleop.disconnect() + robot.disconnect() + + +def main(): + register_third_party_devices() + test_pipeline() + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_full_robot_state.py b/additional_functions/test_full_robot_state.py new file mode 100644 index 000000000..8bc2f46d0 --- /dev/null +++ b/additional_functions/test_full_robot_state.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +""" +Test script for get_full_robot_state() function. +Displays IMU data and motor states from the G1 robot. + +Usage: + python test_full_robot_state.py +""" + +import time +import logging +import numpy as np +from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1 +from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + + +def print_robot_state(state: dict): + """Print robot state in a readable format""" + + print("\n" + "=" * 70) + print("G1 FULL ROBOT STATE") + print("=" * 70) + + # IMU Data + imu = state['imu'] + print(f"\n🧭 IMU:") + print(f" Orientation (deg): Roll={np.degrees(imu['rpy'][0]):+.1f}°, " + f"Pitch={np.degrees(imu['rpy'][1]):+.1f}°, Yaw={np.degrees(imu['rpy'][2]):+.1f}°") + print(f" Gyroscope (rad/s): x={imu['gyroscope'][0]:+.3f}, " + f"y={imu['gyroscope'][1]:+.3f}, z={imu['gyroscope'][2]:+.3f}") + print(f" Accel (m/s²): x={imu['accelerometer'][0]:+.3f}, " + f"y={imu['accelerometer'][1]:+.3f}, z={imu['accelerometer'][2]:+.3f}") + print(f" Quaternion: w={imu['quaternion'][0]:.3f}, " + f"x={imu['quaternion'][1]:+.3f}, y={imu['quaternion'][2]:+.3f}, z={imu['quaternion'][3]:+.3f}") + print(f" Temperature: {imu['temperature']}°C") + + # Motor Data - show first 5 motors + motors = state['motors'] + print(f"\n🦾 Motors (showing first 5 of {len(motors)}):") + for motor in motors[:5]: + print(f" Motor {motor['id']:2d}: pos={motor['q']:+.3f} rad, " + f"vel={motor['dq']:+.3f} rad/s, " + f"torque={motor['tau_est']:+.2f} Nm, " + f"temp={motor['temperature']}°C") + + # Motor Statistics + print(f"\n📊 Motor Statistics (all {len(motors)} motors):") + temps = [m['temperature'] for m in motors] + torques = [abs(m['tau_est']) for m in motors] + velocities = [abs(m['dq']) for m in motors] + + print(f" Temperature: min={min(temps)}°C, max={max(temps)}°C, avg={sum(temps)/len(temps):.1f}°C") + print(f" Torque: min={min(torques):.2f}Nm, max={max(torques):.2f}Nm, avg={sum(torques)/len(torques):.2f}Nm") + print(f" Velocity: min={min(velocities):.3f}rad/s, max={max(velocities):.3f}rad/s") + + # High torque warning + high_torque = [(m['id'], m['tau_est']) for m in motors if abs(m['tau_est']) > 5.0] + if high_torque: + print(f"\n⚠️ Motors with high torque (>5.0 Nm):") + for motor_id, torque in high_torque[:10]: # Show up to 10 + print(f" Motor {motor_id:2d}: {torque:+.2f} Nm") + + print("\nPress Ctrl+C to stop") + + +def main(): + print("="*70) + print("G1 Robot Full State Test") + print("="*70) + + # Create robot config + config = UnitreeG1Config( + cameras={}, # No cameras needed for state test + motion_mode=False, + simulation_mode=False, + ) + + # Initialize robot + print("\nInitializing robot...") + robot = UnitreeG1(config) + + # Give it a moment to start receiving data + time.sleep(1) + + try: + print("\nReading robot state (updating every 0.5 seconds)...") + print("This demonstrates the get_full_robot_state() function") + + while True: + # Get full robot state + state = robot.get_full_robot_state() + + # Print it + print_robot_state(state) + + # Wait before next update + time.sleep(0.5) + + except KeyboardInterrupt: + print("\n\nStopped by user") + + except Exception as e: + print(f"\n✗ Error: {e}") + import traceback + traceback.print_exc() + + finally: + print("\nDisconnecting robot...") + robot.disconnect() + print("Done!") + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_led_control.py b/additional_functions/test_led_control.py new file mode 100644 index 000000000..124988557 --- /dev/null +++ b/additional_functions/test_led_control.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +""" +Test script for G1 robot LED control using the new audio_control function. + +Usage: + python test_led_control.py +""" + +import time +import logging +from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1 +from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config + +# Set up logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +def main(): + print("="*60) + print("G1 Robot LED Control Test") + print("="*60) + + # Create robot config + config = UnitreeG1Config( + cameras={}, # No cameras needed for LED test + motion_mode=False, + simulation_mode=False, + ) + + # Initialize robot + print("\n[1/4] Initializing robot...") + robot = UnitreeG1(config) + + try: + # Test LED: Red + print("\n[2/4] Setting LED to RED (255, 0, 0)...") + robot.audio_control((255, 0, 0)) + time.sleep(2) + + # Test LED: Green + print("\n[3/4] Setting LED to GREEN (0, 255, 0)...") + robot.audio_control((0, 255, 0)) + time.sleep(2) + + # Test LED: Blue + print("\n[4/4] Setting LED to BLUE (0, 0, 255)...") + robot.audio_control((0, 0, 255)) + time.sleep(2) + + # Test TTS + print("\n[Bonus 1] Testing text-to-speech...") + robot.audio_control("LED test complete!", volume=80) + time.sleep(3) + + # Test WAV playback + print("\n[Bonus 2] Testing WAV file playback...") + wav_file = "out.wav" + import os + if os.path.exists(wav_file): + robot.audio_control(wav_file, volume=80) + print(f"Playing {wav_file}... (waiting for playback to complete)") + # Wait a bit for audio to play (adjust based on your file length) + time.sleep(5) + else: + print(f"⚠ Warning: {wav_file} not found, skipping WAV playback test") + print(f" To test WAV playback, place a 16kHz mono 16-bit WAV file named 'out.wav' in this directory") + + print("\n" + "="*60) + print("✓ LED control test completed successfully!") + print("="*60) + + except Exception as e: + print(f"\n✗ Error during test: {e}") + import traceback + traceback.print_exc() + + finally: + print("\nDisconnecting robot...") + robot.disconnect() + print("Done!") + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_mode_sequence.py b/additional_functions/test_mode_sequence.py new file mode 100644 index 000000000..cff68c9f2 --- /dev/null +++ b/additional_functions/test_mode_sequence.py @@ -0,0 +1,286 @@ +#!/usr/bin/env python3 +""" +Test script to transition through robot modes: zero torque → damp → start → balance stand. +This script demonstrates a sequence of robot mode transitions. +""" + +import time +import sys +import signal + +# Add the unitree_sdk2_python path to sys.path +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import MotionSwitcherClient +from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient + + +class ModeSequenceTester: + def __init__(self): + # Initialize motion switcher client first + self.msc = MotionSwitcherClient() + self.msc.SetTimeout(5.0) + self.msc.Init() + + # Initialize locomotion client + self.loco_client = LocoClient() + self.loco_client.SetTimeout(10.0) + self.loco_client.Init() + self.running = True + + # Set up signal handler for graceful shutdown + signal.signal(signal.SIGINT, self.signal_handler) + + def signal_handler(self, sig, frame): + """Handle Ctrl+C gracefully.""" + print("\nReceived interrupt signal. Shutting down...") + self.running = False + # Just exit immediately + exit(0) + + def reset_robot_state(self): + """Reset robot to a clean state before starting sequence.""" + try: + print("Resetting robot to clean state...") + + # Check current mode first + try: + status, result = self.msc.CheckMode() + if status == 0: + print(f"📊 Current mode: {result.get('name', 'unknown')}") + # Release current mode if any + if result.get('name'): + print(" Releasing current mode...") + self.msc.ReleaseMode() + time.sleep(2.0) + except Exception as e: + print(f" Could not check/release mode: {e}") + + # Force reset through locomotion + print(" Setting to damp mode...") + self.loco_client.Damp() + time.sleep(2.0) + + print(" Setting to zero torque...") + self.loco_client.ZeroTorque() + time.sleep(2.0) + + print(" Back to damp mode...") + self.loco_client.Damp() + time.sleep(2.0) + + print(" Robot state reset complete") + except Exception as e: + print(f" Warning: Could not fully reset robot state: {e}") + # Try minimal reset + try: + print(" Attempting minimal reset...") + self.loco_client.Damp() + time.sleep(1.0) + print(" Minimal reset complete") + except Exception as e2: + print(f" Even minimal reset failed: {e2}") + + def cleanup_and_reset(self): + """Clean up and reset robot after sequence.""" + try: + print(" Cleaning up and resetting robot...") + + # Release any held modes (but don't force damp mode) + try: + print(" Releasing any held modes...") + self.msc.ReleaseMode() + time.sleep(1.0) + except Exception as e: + print(f" Could not release mode: {e}") + + print(" Cleanup complete - robot ready for next commands") + except Exception as e: + print(f" Warning: Could not clean up robot state: {e}") + print(" Cleanup attempted - robot may still be ready") + + def switch_to_zero_torque(self): + """Switch robot to zero torque mode.""" + try: + print(" Switching to ZERO TORQUE mode (FSM ID: 0)...") + self.loco_client.ZeroTorque() + return True + except Exception as e: + print(f" Error switching to zero torque mode: {e}") + return False + + def switch_to_damp(self): + """Switch robot to damping mode.""" + try: + print(" Switching to DAMP mode (FSM ID: 1)...") + self.loco_client.Damp() + return True + except Exception as e: + print(f" Error switching to damp mode: {e}") + return False + + def switch_to_start(self): + """Switch robot to start mode.""" + try: + print(" Switching to START mode (FSM ID: 200)...") + self.loco_client.Start() + return True + except Exception as e: + print(f" Error switching to start mode: {e}") + return False + + def switch_to_high_stand(self): + """Switch robot to high stand mode.""" + try: + print(" Switching to HIGH STAND mode (max height)...") + self.loco_client.HighStand() + return True + except Exception as e: + print(f" Error switching to high stand mode: {e}") + return False + + + def switch_to_low_stand(self): + """Switch robot to low stand mode.""" + try: + print(" Switching to LOW STAND mode...") + self.loco_client.LowStand() + return True + except Exception as e: + print(f" Error switching to low stand mode: {e}") + return False + + def switch_to_squat2standup(self): + """Switch robot to squat2standup mode.""" + try: + print(" Switching to SQUAT2STANDUP mode (FSM ID: 706)...") + self.loco_client.Squat2StandUp() + return True + except Exception as e: + print(f" Error switching to squat2standup mode: {e}") + return False + + def switch_to_lie2standup(self): + """Switch robot to lie2standup mode.""" + try: + print(" Switching to LIE2STANDUP mode (FSM ID: 702)...") + self.loco_client.Lie2StandUp() + return True + except Exception as e: + print(f" Error switching to lie2standup mode: {e}") + return False + + def switch_to_wave_hand(self, turn_flag=False): + """Switch robot to wave hand mode.""" + try: + print(f" Switching to WAVE HAND mode (turn_flag: {turn_flag})...") + self.loco_client.WaveHand(turn_flag) + return True + except Exception as e: + print(f" Error switching to wave hand mode: {e}") + return False + + def switch_to_shake_hand(self, stage=-1): + """Switch robot to shake hand mode.""" + try: + print(f" Switching to SHAKE HAND mode (stage: {stage})...") + self.loco_client.ShakeHand(stage) + return True + except Exception as e: + print(f" Error switching to shake hand mode: {e}") + return False + + def interactive_control(self): + """Interactive keyboard control for robot modes.""" + print("=" * 70) + print("G1 Robot Interactive Control") + print("=" * 70) + print("Commands:") + print(" 0 = Zero Torque (FSM ID: 0)") + print(" 1 = Damp (FSM ID: 1)") + print(" 2 = Start (FSM ID: 200)") + print(" 3 = High Stand") + print(" 4 = Low Stand") + print(" 5 = Squat2StandUp") + print(" 6 = Lie2StandUp") + print(" 7 = Wave Hand") + print(" 8 = Shake Hand") + print(" q = Quit") + print("\nWARNING: Ensure robot is in a safe position!") + print("=" * 70) + + while self.running: + try: + command = input("\nEnter command (0-8 or q): ").strip() + + if command == 'q': + print("Quitting...") + break + elif command == '0': + self.switch_to_zero_torque() + elif command == '1': + self.switch_to_damp() + elif command == '2': + self.switch_to_start() + elif command == '3': + self.switch_to_high_stand() + elif command == '4': + self.switch_to_low_stand() + elif command == '5': + self.switch_to_squat2standup() + elif command == '6': + self.switch_to_lie2standup() + elif command == '7': + self.switch_to_wave_hand() + elif command == '8': + self.switch_to_shake_hand() + else: + print("Invalid command. Try 0-8 or q.") + + except KeyboardInterrupt: + print("\nReceived interrupt signal. Shutting down...") + break + except Exception as e: + print(f"Error: {e}") + + print("Interactive control finished.") + + +def main(): + print("G1 Robot Interactive Control") + print("This script provides keyboard control for robot modes") + + # Get network interface from command line or use default + if len(sys.argv) > 1: + network_interface = sys.argv[1] + print(f"Using network interface: {network_interface}") + else: + network_interface = "en7" # Default based on your setup + print(f"Using default network interface: {network_interface}") + + print("\nInitializing connection...") + + try: + # Initialize the channel factory + ChannelFactoryInitialize(0, network_interface) + print(" Channel factory initialized") + + # Create tester + tester = ModeSequenceTester() + print(" LocoClient created") + + # Run interactive control + tester.interactive_control() + + except Exception as e: + print(f" Error during initialization: {e}") + print("\n🔧 Troubleshooting:") + print(" - Make sure you're connected to the robot's network") + print(" - Try: sudo ifconfig en7 192.168.123.100 netmask 255.255.255.0") + print(" - Verify robot is powered on and accessible") + print(" - Check if robot is in 'ai' mode") + + +if __name__ == "__main__": + main() diff --git a/additional_functions/test_robot_mode.py b/additional_functions/test_robot_mode.py new file mode 100644 index 000000000..46eaf2e54 --- /dev/null +++ b/additional_functions/test_robot_mode.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +""" +Test script to check G1 robot's current mode and print it to screen. +This script uses MotionSwitcherClient to check the robot's control mode. +""" + +import time +import sys +import json + +# Add the unitree_sdk2_python path to sys.path +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import MotionSwitcherClient + + +class RobotModeChecker: + def __init__(self): + self.msc = MotionSwitcherClient() + self.msc.SetTimeout(5.0) + self.msc.Init() + + def check_mode(self): + """Check the robot's current mode and return the result.""" + try: + status, result = self.msc.CheckMode() + return status, result + except Exception as e: + print(f"Error checking mode: {e}") + return -1, None + + def print_mode_info(self): + """Print detailed mode information to screen.""" + print("=" * 50) + print("G1 Robot Mode Checker") + print("=" * 50) + + print("\nChecking robot mode...") + status, result = self.check_mode() + + if status == 0 and result is not None: + print("✅ Successfully connected to robot!") + print(f"📊 Status Code: {status}") + print(f"📋 Mode Result: {json.dumps(result, indent=2)}") + + # Extract mode name if available + if isinstance(result, dict) and 'name' in result: + mode_name = result['name'] + if mode_name: + print(f"🤖 Current Mode: {mode_name}") + + # Provide mode interpretation + mode_interpretations = { + 'ai': 'AI/Autonomous Mode - Ready for external commands', + 'normal': 'Normal Mode - Basic operation mode', + 'advanced': 'Advanced Mode - Advanced control mode', + 'ai-w': 'AI-Wheeled Mode - For wheeled robots', + '': 'Safe Mode - No active control mode' + } + + interpretation = mode_interpretations.get(mode_name, 'Unknown Mode') + print(f"💡 Interpretation: {interpretation}") + else: + print("🤖 Current Mode: Safe Mode (No active mode)") + print("💡 Interpretation: Robot is in safe state, ready to accept commands") + else: + print("🤖 Current Mode: Unknown (check result structure)") + + elif status == -1: + print("❌ Failed to connect to robot") + print("🔧 Troubleshooting:") + print(" - Check network connection") + print(" - Verify robot is powered on") + print(" - Ensure correct network interface (try en7)") + print(" - Check if robot IP is 192.168.123.164") + else: + print(f"❌ Error checking mode. Status: {status}") + if result: + print(f"📋 Result: {result}") + + print("\n" + "=" * 50) + + +def main(): + print("G1 Robot Mode Checker") + print("This script will check the robot's current control mode.") + + # Get network interface from command line or use default + if len(sys.argv) > 1: + network_interface = sys.argv[1] + print(f"Using network interface: {network_interface}") + else: + network_interface = "en7" # Default based on your setup + print(f"Using default network interface: {network_interface}") + + print("\nInitializing connection...") + + try: + # Initialize the channel factory + ChannelFactoryInitialize(0, network_interface) + print("✅ Channel factory initialized") + + # Create mode checker + checker = RobotModeChecker() + print("✅ MotionSwitcherClient created") + + # Check and print mode + checker.print_mode_info() + + # Optionally check mode multiple times + print("\n🔄 Checking mode again in 2 seconds...") + time.sleep(2) + checker.print_mode_info() + + except Exception as e: + print(f"❌ Error during initialization: {e}") + print("\n🔧 Troubleshooting:") + print(" - Make sure you're connected to the robot's network") + print(" - Try: sudo ifconfig en7 192.168.123.100 netmask 255.255.255.0") + print(" - Verify robot is powered on and accessible") + + +if __name__ == "__main__": + main() diff --git a/additional_functions/test_sensors.py b/additional_functions/test_sensors.py new file mode 100644 index 000000000..41e5c974a --- /dev/null +++ b/additional_functions/test_sensors.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +""" +Quick G1 sensor test script. +Displays IMU, motor states, and system info. + +Usage: + python test_sensors.py + Example: python test_sensors.py en7 +""" + +import sys +import time +import numpy as np +from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_ + + +def print_sensor_data(msg: LowState_): + """Print sensor data in a readable format""" + + print("\n" + "=" * 70) + print(f"G1 SENSOR DATA @ tick {msg.tick}") + print("=" * 70) + + # IMU + imu = msg.imu_state + print(f"\n🧭 IMU:") + print(f" Orientation (deg): Roll={np.degrees(imu.rpy[0]):+.1f}°, " + f"Pitch={np.degrees(imu.rpy[1]):+.1f}°, Yaw={np.degrees(imu.rpy[2]):+.1f}°") + print(f" Gyroscope (rad/s): x={imu.gyroscope[0]:+.3f}, " + f"y={imu.gyroscope[1]:+.3f}, z={imu.gyroscope[2]:+.3f}") + print(f" Accel (m/s²): x={imu.accelerometer[0]:+.3f}, " + f"y={imu.accelerometer[1]:+.3f}, z={imu.accelerometer[2]:+.3f}") + print(f" Quaternion: w={imu.quaternion[0]:.3f}, " + f"x={imu.quaternion[1]:+.3f}, y={imu.quaternion[2]:+.3f}, z={imu.quaternion[3]:+.3f}") + print(f" Temperature: {imu.temperature}°C") + + # Motors - show first 5 and summary + print(f"\n🦾 Motors (showing first 5 of 35):") + for i in range(min(5, 35)): + motor = msg.motor_state[i] + print(f" Motor {i:2d}: pos={motor.q:+.3f} rad, " + f"vel={motor.dq:+.3f} rad/s, " + f"torque={motor.tau_est:+.2f} Nm, " + f"temp={motor.temperature[0]}°C") + + # Motor statistics + print(f"\n📊 Motor Statistics (all 35):") + temps = [msg.motor_state[i].temperature[0] for i in range(35)] + torques = [abs(msg.motor_state[i].tau_est) for i in range(35)] + velocities = [abs(msg.motor_state[i].dq) for i in range(35)] + + print(f" Temperature: min={min(temps)}°C, max={max(temps)}°C, avg={sum(temps)/len(temps):.1f}°C") + print(f" Torque: min={min(torques):.2f}Nm, max={max(torques):.2f}Nm, avg={sum(torques)/len(torques):.2f}Nm") + print(f" Velocity: min={min(velocities):.3f}rad/s, max={max(velocities):.3f}rad/s") + + # High torque warning + high_torque = [(i, msg.motor_state[i].tau_est) for i in range(35) + if abs(msg.motor_state[i].tau_est) > 5.0] + if high_torque: + print(f"\n⚠️ Motors with high torque (>5.0 Nm):") + for motor_id, torque in high_torque[:10]: # Show up to 10 + print(f" Motor {motor_id:2d}: {torque:+.2f} Nm") + + # System + print(f"\n⚙️ System Info:") + print(f" Mode: machine={msg.mode_machine}, pr={msg.mode_pr}") + print(f" Version: {msg.version[0]}.{msg.version[1]}") + + print("\nPress Ctrl+C to stop") + + +def main(): + if len(sys.argv) < 2: + print(f"Usage: python3 {sys.argv[0]} ") + print(f"Example: python3 {sys.argv[0]} en7") + sys.exit(1) + + network_interface = sys.argv[1] + + print(f"Initializing DDS on {network_interface}...") + ChannelFactoryInitialize(0, network_interface) + + print("Subscribing to rt/lowstate...") + lowstate_sub = ChannelSubscriber("rt/lowstate", LowState_) + lowstate_sub.Init(print_sensor_data, 10) + + print("Receiving sensor data...") + print("(Data will print each time it's received)") + + try: + while True: + time.sleep(1) # Keep alive, handler prints on each message + except KeyboardInterrupt: + print("\n\nStopped") + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_speaker.py b/additional_functions/test_speaker.py new file mode 100644 index 000000000..bb0f943e2 --- /dev/null +++ b/additional_functions/test_speaker.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +""" +Simple test script to use the G1 robot's speaker. +Demonstrates TTS (text-to-speech) and volume control. + +Usage: + python test_speaker.py en7 + (replace 'en7' with your network interface) +""" + +import sys +import time +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient + + +def main(): + if len(sys.argv) < 2: + print(f"Usage: python3 {sys.argv[0]} ") + print("Example: python3 test_speaker.py en7") + sys.exit(1) + + # Initialize DDS communication + network_interface = sys.argv[1] + print(f"Initializing communication on {network_interface}...") + ChannelFactoryInitialize(0, network_interface) + + # Create audio client + audio_client = AudioClient() + audio_client.SetTimeout(10.0) + audio_client.Init() + print("Audio client initialized!") + + # Get current volume + code, volume_data = audio_client.GetVolume() + if code == 0: + print(f"Current volume: {volume_data}") + + # Set volume to 80% + print("Setting volume to 80%...") + audio_client.SetVolume(100) + time.sleep(0.5) + + # # Test English TTS + # print("Speaking (English)...") + audio_client.LedControl(255, 0, 0) + audio_client.TtsMaker("Hello!", 0) + exit() + # time.sleep(5) + + # # Test Chinese TTS + # print("Speaking (Chinese)...") + # audio_client.TtsMaker("大家好！我是宇树科技人形机器人。", 0) + # time.sleep(5) + + # # Test LED control (bonus!) + # print("Testing LED strip...") + # audio_client.TtsMaker("Now testing LED lights.", 0) + # time.sleep(2) + + print("LED: Red") + # Red + time.sleep(1) + + print("LED: Green") + #audio_client.LedControl(0, 255, 0) # Green + time.sleep(1) + + print("LED: Blue") + #audio_client.LedControl(0, 0, 255) # Blue + time.sleep(1) + + + print("Done!") + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_speaker_wav.py b/additional_functions/test_speaker_wav.py new file mode 100644 index 000000000..e13de38cd --- /dev/null +++ b/additional_functions/test_speaker_wav.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +Play a WAV file through the G1 robot's speaker. + +Requirements: + - WAV file must be 16kHz, mono, 16-bit PCM + +Usage: + python test_speaker_wav.py en7 path/to/audio.wav +""" + +import sys +import time +import struct +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient + + +def read_wav(filename): + """Read WAV file and return PCM data.""" + try: + with open(filename, 'rb') as f: + def read(fmt): + return struct.unpack(fmt, f.read(struct.calcsize(fmt))) + + # Read RIFF header + chunk_id, = read(' ") + print("Example: python3 test_speaker_wav.py en7 audio.wav") + print("\nWAV file requirements:") + print(" - Sample rate: 16000 Hz") + print(" - Channels: 1 (mono)") + print(" - Bit depth: 16-bit") + sys.exit(1) + + network_interface = sys.argv[1] + wav_path = sys.argv[2] + + # Initialize communication + print(f"Initializing on {network_interface}...") + ChannelFactoryInitialize(0) + + # Create audio client + audio_client = AudioClient() + audio_client.SetTimeout(10.0) + audio_client.Init() + print("Audio client initialized!") + + # Read WAV file + print(f"Reading WAV file: {wav_path}") + pcm_list, sample_rate, num_channels, is_ok = read_wav(wav_path) + + if not is_ok: + print("[ERROR] Failed to read WAV file") + sys.exit(1) + + print(f"WAV info:") + print(f" - Sample rate: {sample_rate} Hz") + print(f" - Channels: {num_channels}") + print(f" - Size: {len(pcm_list)} bytes") + + # Verify format + if sample_rate != 16000: + print(f"[ERROR] Sample rate must be 16000 Hz, got {sample_rate} Hz") + print("Use ffmpeg to convert:") + print(f" ffmpeg -i input.wav -ar 16000 -ac 1 -sample_fmt s16 output.wav") + sys.exit(1) + + if num_channels != 1: + print(f"[ERROR] Must be mono (1 channel), got {num_channels} channels") + print("Use ffmpeg to convert:") + print(f" ffmpeg -i input.wav -ar 16000 -ac 1 -sample_fmt s16 output.wav") + sys.exit(1) + + # Play audio + print("Playing audio...") + play_pcm_stream(audio_client, pcm_list, "test_wav") + + # Wait for playback to finish + duration_seconds = len(pcm_list) / (16000 * 2) # 16kHz, 16-bit (2 bytes) + print(f"Waiting {duration_seconds:.1f} seconds for playback...") + time.sleep(duration_seconds + 1) + + # Stop playback + print("Stopping playback...") + audio_client.PlayStop("test_wav") + + print("Done!") + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_teleop_only.py b/additional_functions/test_teleop_only.py new file mode 100644 index 000000000..caaf567ac --- /dev/null +++ b/additional_functions/test_teleop_only.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Test script to measure teleoperator read performance in isolation. +This script ONLY reads from the teleoperator without initializing or communicating with a robot. + +Example: + +```shell +python test_teleop_only.py \ + --teleop.type=custom \ + --fps=60 \ + --duration=10.0 +``` + +""" + +import logging +import time +from dataclasses import asdict, dataclass +from pprint import pformat + +from lerobot.configs import parser +from lerobot.teleoperators import ( # noqa: F401 + Teleoperator, + TeleoperatorConfig, + bi_so100_leader, + gamepad, + homunculus, + koch_leader, + make_teleoperator_from_config, + so100_leader, + so101_leader, + custom +) +from lerobot.utils.import_utils import register_third_party_devices +from lerobot.utils.robot_utils import busy_wait +from lerobot.utils.utils import init_logging, move_cursor_up + + +@dataclass +class TestTeleopConfig: + teleop: TeleoperatorConfig + # Limit the maximum frames per second. + fps: int = 60 + # Test duration in seconds + duration: float = 10.0 + # Display detailed timing stats + display_stats: bool = True + + +def test_teleop_loop( + teleop: Teleoperator, + fps: int, + duration: float, + display_stats: bool = True, +): + """ + Test loop that only reads from teleoperator and measures performance. + + Args: + teleop: The teleoperator device instance providing control actions. + fps: The target frequency for the control loop in frames per second. + duration: The duration of the test in seconds. + display_stats: If True, displays detailed timing statistics. + """ + + start = time.perf_counter() + iteration = 0 + total_read_time = 0 + min_read_time = float('inf') + max_read_time = 0 + + print(f"\nStarting teleoperator read test for {duration}s at {fps} FPS target...") + print(f"Teleoperator: {teleop.__class__.__name__}") + print(f"Action features: {list(teleop.action_features.keys())}") + print(f"Number of actions: {len(teleop.action_features)}") + print("-" * 80) + + while True: + loop_start = time.perf_counter() + + # Measure just the get_action call + read_start = time.perf_counter() + action = teleop.get_action() + read_time = time.perf_counter() - read_start + + # Track statistics + total_read_time += read_time + min_read_time = min(min_read_time, read_time) + max_read_time = max(max_read_time, read_time) + iteration += 1 + + if display_stats: + # Display timing information + avg_read_time = total_read_time / iteration + print(f"\nIteration: {iteration}") + print(f"Read time: {read_time * 1e3:>7.2f}ms") + print(f"Avg read time: {avg_read_time * 1e3:>7.2f}ms") + print(f"Min read time: {min_read_time * 1e3:>7.2f}ms") + print(f"Max read time: {max_read_time * 1e3:>7.2f}ms") + + # Show only the wrist roll actions + print("\nWrist Roll Actions:") + print(f" kLeftWristRoll.pos: {action.get('kLeftWristRoll.pos', 'N/A'):.4f}") + print(f" kRightWristRoll.pos: {action.get('kRightWristRoll.pos', 'N/A'):.4f}") + + move_cursor_up(9) + + # Wait to maintain target FPS + dt_s = time.perf_counter() - loop_start + busy_wait(1 / fps - dt_s) + + loop_time = time.perf_counter() - loop_start + actual_fps = 1 / loop_time if loop_time > 0 else 0 + + # Check if duration reached + elapsed = time.perf_counter() - start + if elapsed >= duration: + break + + # Print final statistics + print("\n" + "=" * 80) + print("FINAL STATISTICS") + print("=" * 80) + print(f"Total iterations: {iteration}") + print(f"Total time: {elapsed:.2f}s") + print(f"Actual FPS: {iteration / elapsed:.2f}") + print(f"Target FPS: {fps}") + print(f"\nget_action() timing:") + print(f" Average: {total_read_time / iteration * 1e3:.2f}ms") + print(f" Min: {min_read_time * 1e3:.2f}ms") + print(f" Max: {max_read_time * 1e3:.2f}ms") + print(f" Total: {total_read_time:.2f}s ({total_read_time/elapsed*100:.1f}% of loop time)") + print("=" * 80) + + +@parser.wrap() +def test_teleop(cfg: TestTeleopConfig): + init_logging() + logging.info(pformat(asdict(cfg))) + + print("\nInitializing teleoperator...") + teleop = make_teleoperator_from_config(cfg.teleop) + + print("Connecting teleoperator...") + teleop.connect() + + print(f"Connected: {teleop.is_connected}") + print(f"Calibrated: {teleop.is_calibrated}") + + try: + test_teleop_loop( + teleop=teleop, + fps=cfg.fps, + duration=cfg.duration, + display_stats=cfg.display_stats, + ) + except KeyboardInterrupt: + print("\nTest interrupted by user") + finally: + print("\nDisconnecting teleoperator...") + teleop.disconnect() + + +def main(): + register_third_party_devices() + test_teleop() + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/test_zmq_camera.py b/additional_functions/test_zmq_camera.py new file mode 100644 index 000000000..d5534235f --- /dev/null +++ b/additional_functions/test_zmq_camera.py @@ -0,0 +1,298 @@ +#!/usr/bin/env python3 +""" +Comprehensive test script for ZMQ camera integration. +Tests all camera functionalities: find_cameras, connect, read, async_read, disconnect. +""" + +import time +import numpy as np +from pathlib import Path + +from lerobot.cameras.zmq import ZMQCamera, ZMQCameraConfig +from lerobot.cameras.configs import ColorMode + + +def test_find_cameras(): + """Test 1: Camera Discovery""" + print("\n" + "="*60) + print("TEST 1: Camera Discovery (find_cameras)") + print("="*60) + + cameras = ZMQCamera.find_cameras() + + if not cameras: + print("❌ No ZMQ cameras found!") + print(" Make sure you have configured cameras in ~/.lerobot/zmq_cameras.json") + print(" or set LEROBOT_ZMQ_CAMERAS environment variable") + return None + + print(f"✅ Found {len(cameras)} ZMQ camera(s):") + for i, cam in enumerate(cameras): + print(f"\n Camera {i}:") + for key, value in cam.items(): + if key == "default_stream_profile": + print(f" {key}:") + for sub_key, sub_value in value.items(): + print(f" {sub_key}: {sub_value}") + else: + print(f" {key}: {value}") + + return cameras[0] if cameras else None + + +def test_connect_disconnect(cam_info): + """Test 2: Connect and Disconnect""" + print("\n" + "="*60) + print("TEST 2: Connect and Disconnect") + print("="*60) + + config = ZMQCameraConfig( + server_address=cam_info["server_address"], + port=cam_info["port"], + camera_name=cam_info["camera_name"], + color_mode=ColorMode.RGB, + ) + + camera = ZMQCamera(config) + + # Test is_connected before connection + print(f"Before connect - is_connected: {camera.is_connected}") + assert not camera.is_connected, "❌ Camera should not be connected initially" + + # Test connect + print("Connecting to camera...") + start = time.time() + camera.connect(warmup=True) + connect_time = time.time() - start + print(f"✅ Connected in {connect_time:.2f}s") + print(f" Camera resolution: {camera.width}x{camera.height}") + assert camera.is_connected, "❌ Camera should be connected after connect()" + + # Test disconnect + print("Disconnecting camera...") + camera.disconnect() + print("✅ Disconnected") + assert not camera.is_connected, "❌ Camera should not be connected after disconnect()" + + return config + + +def test_synchronous_read(config): + """Test 3: Synchronous Read""" + print("\n" + "="*60) + print("TEST 3: Synchronous Read (read)") + print("="*60) + + camera = ZMQCamera(config) + camera.connect(warmup=False) + + print("Reading 10 frames synchronously...") + read_times = [] + + for i in range(10): + start = time.time() + frame = camera.read() + read_time = (time.time() - start) * 1000 # ms + read_times.append(read_time) + + # Validate frame + assert isinstance(frame, np.ndarray), "❌ Frame should be numpy array" + assert frame.ndim == 3, "❌ Frame should be 3D (H, W, C)" + assert frame.shape[2] == 3, "❌ Frame should have 3 channels" + + if i == 0: + print(f" Frame shape: {frame.shape}") + print(f" Frame dtype: {frame.dtype}") + print(f" Frame range: [{frame.min()}, {frame.max()}]") + + avg_time = np.mean(read_times) + std_time = np.std(read_times) + fps = 1000 / avg_time if avg_time > 0 else 0 + + print(f"✅ Read 10 frames successfully") + print(f" Average read time: {avg_time:.2f} ± {std_time:.2f} ms") + print(f" Estimated FPS: {fps:.1f}") + + camera.disconnect() + + +def test_color_mode_conversion(config): + """Test 4: Color Mode Conversion""" + print("\n" + "="*60) + print("TEST 4: Color Mode Conversion") + print("="*60) + + # Test RGB mode + config_rgb = ZMQCameraConfig( + server_address=config.server_address, + port=config.port, + camera_name=config.camera_name, + color_mode=ColorMode.RGB, + ) + camera_rgb = ZMQCamera(config_rgb) + camera_rgb.connect(warmup=False) + frame_rgb = camera_rgb.read() + camera_rgb.disconnect() + + # Test BGR mode + config_bgr = ZMQCameraConfig( + server_address=config.server_address, + port=config.port, + camera_name=config.camera_name, + color_mode=ColorMode.BGR, + ) + camera_bgr = ZMQCamera(config_bgr) + camera_bgr.connect(warmup=False) + frame_bgr = camera_bgr.read() + camera_bgr.disconnect() + + # Frames should be different (color channels swapped) + assert not np.array_equal(frame_rgb, frame_bgr), "❌ RGB and BGR frames should differ" + # But shapes should be the same + assert frame_rgb.shape == frame_bgr.shape, "❌ RGB and BGR frames should have same shape" + + print("✅ RGB mode works correctly") + print("✅ BGR mode works correctly") + print(f" Frame shapes match: {frame_rgb.shape}") + + +def test_asynchronous_read(config): + """Test 5: Asynchronous Read""" + print("\n" + "="*60) + print("TEST 5: Asynchronous Read (async_read)") + print("="*60) + + camera = ZMQCamera(config) + camera.connect(warmup=False) + + print("Reading 10 frames asynchronously...") + read_times = [] + + for i in range(10): + start = time.time() + frame = camera.async_read(timeout_ms=1000) + read_time = (time.time() - start) * 1000 # ms + read_times.append(read_time) + + # Validate frame + assert isinstance(frame, np.ndarray), "❌ Frame should be numpy array" + assert frame.ndim == 3, "❌ Frame should be 3D (H, W, C)" + + if i == 0: + print(f" Frame shape: {frame.shape}") + print(f" Background thread alive: {camera.thread.is_alive()}") + + avg_time = np.mean(read_times) + std_time = np.std(read_times) + fps = 1000 / avg_time if avg_time > 0 else 0 + + print(f"✅ Read 10 frames asynchronously") + print(f" Average read time: {avg_time:.2f} ± {std_time:.2f} ms") + print(f" Estimated FPS: {fps:.1f}") + + camera.disconnect() + + +def test_reconnection(config): + """Test 6: Reconnection""" + print("\n" + "="*60) + print("TEST 6: Reconnection") + print("="*60) + + camera = ZMQCamera(config) + + # Connect, read, disconnect cycle 1 + print("Connection cycle 1...") + camera.connect(warmup=False) + frame1 = camera.read() + camera.disconnect() + + # Wait a bit + time.sleep(0.5) + + # Connect, read, disconnect cycle 2 + print("Connection cycle 2...") + camera.connect(warmup=False) + frame2 = camera.read() + camera.disconnect() + + # Frames should be valid + assert frame1.shape == frame2.shape, "❌ Frames should have consistent shape" + + print("✅ Reconnection works correctly") + print(f" Both frames have shape: {frame1.shape}") + + +def test_timeout_handling(config): + """Test 7: Timeout Handling""" + print("\n" + "="*60) + print("TEST 7: Timeout Handling") + print("="*60) + + # Create config with very short timeout + config_short = ZMQCameraConfig( + server_address=config.server_address, + port=config.port, + camera_name=config.camera_name, + timeout_ms=50, # Very short timeout + ) + + camera = ZMQCamera(config_short) + camera.connect(warmup=False) + + try: + # This should work if server is fast enough + frame = camera.read() + print(f"✅ Read succeeded even with {config_short.timeout_ms}ms timeout") + except TimeoutError: + print(f"⚠️ Timeout occurred (expected with {config_short.timeout_ms}ms timeout)") + + camera.disconnect() + + +def main(): + """Run all tests""" + print("\n" + "="*60) + print("ZMQ CAMERA COMPREHENSIVE TEST SUITE") + print("="*60) + + try: + # Test 1: Discovery + cam_info = test_find_cameras() + if not cam_info: + print("\n❌ Cannot proceed without configured cameras") + return + + # Test 2: Connect/Disconnect + config = test_connect_disconnect(cam_info) + + # Test 3: Synchronous Read + test_synchronous_read(config) + + # Test 4: Color Mode + test_color_mode_conversion(config) + + # Test 5: Asynchronous Read + test_asynchronous_read(config) + + # Test 6: Reconnection + test_reconnection(config) + + # Test 7: Timeout + test_timeout_handling(config) + + # Summary + print("\n" + "="*60) + print("✅ ALL TESTS PASSED!") + print("="*60) + print("\nZMQ Camera integration is working correctly! 🎉") + + except Exception as e: + print(f"\n❌ TEST FAILED: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + main() + diff --git a/additional_functions/view_camera.py b/additional_functions/view_camera.py new file mode 100644 index 000000000..9175ce0a0 --- /dev/null +++ b/additional_functions/view_camera.py @@ -0,0 +1,144 @@ +""" +Bigger display version of camera viewer. +Use --scale to control how big the camera windows are. +Example: + python unitree_lerobot/eval_robot/view_camera.py --scale 1.5 +""" + +import time +import cv2 +import argparse +import numpy as np +from unitree_lerobot.eval_robot.make_robot import setup_image_client + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def resize_for_display(image: np.ndarray, scale: float) -> np.ndarray: + """Resize image by a scale factor for larger display.""" + h, w = image.shape[:2] + new_w = int(w * scale) + new_h = int(h * scale) + return cv2.resize(image, (new_w, new_h)) + + +def view_camera_main(): + parser = argparse.ArgumentParser() + parser.add_argument("--scale", type=float, default=1.5, help="Resize factor for display windows") + args = parser.parse_args() + + # Create minimal config (no robot control needed) + cfg = argparse.Namespace( + sim=False, # Real robot (not simulation) + arm="G1_29", # Doesn't matter for camera-only + ee="dex3", # Doesn't matter for camera-only + motion=False + ) + + scale_factor = args.scale + + logger_mp.info("Setting up image client...") + logger_mp.info("Make sure image_server.py is running on the robot!") + logger_mp.info("Default robot IP: 192.168.123.164:5555") + + image_info = setup_image_client(cfg) + tv_img_array = image_info["tv_img_array"] + wrist_img_array = image_info["wrist_img_array"] + tv_img_shape = image_info["tv_img_shape"] + wrist_img_shape = image_info["wrist_img_shape"] + is_binocular = image_info["is_binocular"] + has_wrist_cam = image_info["has_wrist_cam"] + + logger_mp.info(f"Camera config:") + logger_mp.info(f" - Head camera: {tv_img_shape}, binocular: {is_binocular}") + logger_mp.info(f" - Wrist camera: {wrist_img_shape if has_wrist_cam else 'Not available'}") + logger_mp.info("") + logger_mp.info("Waiting for first frame...") + + time.sleep(2) + + logger_mp.info("=" * 60) + logger_mp.info("CAMERA VIEWER ACTIVE") + logger_mp.info("=" * 60) + logger_mp.info(f"Scale factor: {scale_factor}") + logger_mp.info("Controls:") + logger_mp.info(" - Press 'q' to quit") + logger_mp.info(" - Press 's' to save current frame as image") + logger_mp.info("=" * 60) + + frame_count = 0 + + try: + while True: + head_image = tv_img_array.copy() + + # HEAD CAMERA + if head_image is not None and head_image.size > 0: + if np.any(head_image): + if is_binocular: + h, w = head_image.shape[:2] + left_head = head_image[:, :w // 2] + right_head = head_image[:, w // 2:] + + left_display = resize_for_display(left_head, scale_factor) + right_display = resize_for_display(right_head, scale_factor) + + cv2.imshow('Left Head Camera', left_display) + cv2.imshow('Right Head Camera', right_display) + else: + display_img = resize_for_display(head_image, scale_factor) + cv2.imshow('Head Camera', display_img) + else: + logger_mp.warning("Received empty frame from head camera") + + # WRIST CAMERA + if has_wrist_cam and wrist_img_array is not None: + wrist_image = wrist_img_array.copy() + if wrist_image is not None and wrist_image.size > 0 and np.any(wrist_image): + h, w = wrist_image.shape[:2] + left_wrist = wrist_image[:, :w // 2] + right_wrist = wrist_image[:, w // 2:] + + left_wrist_display = resize_for_display(left_wrist, scale_factor) + right_wrist_display = resize_for_display(right_wrist, scale_factor) + + cv2.imshow('Left Wrist Camera', left_wrist_display) + cv2.imshow('Right Wrist Camera', right_wrist_display) + + # Keyboard input + key = cv2.waitKey(1) & 0xFF + if key == ord('q'): + logger_mp.info("Quit command received") + break + elif key == ord('s'): + timestamp = time.strftime("%Y%m%d_%H%M%S") + cv2.imwrite(f'head_camera_{timestamp}.jpg', head_image) + logger_mp.info(f"Saved: head_camera_{timestamp}.jpg") + if has_wrist_cam and wrist_img_array is not None: + wrist_image = wrist_img_array.copy() + if wrist_image is not None and wrist_image.size > 0: + cv2.imwrite(f'wrist_camera_{timestamp}.jpg', wrist_image) + logger_mp.info(f"Saved: wrist_camera_{timestamp}.jpg") + + frame_count += 1 + if frame_count % 30 == 0: + logger_mp.info(f"Frames received: {frame_count}") + + time.sleep(0.033) + + except KeyboardInterrupt: + logger_mp.info("\nInterrupted by user (Ctrl+C)") + + finally: + logger_mp.info("Closing camera viewer...") + cv2.destroyAllWindows() + #from lerobot.robots.unitree_g1.eval_robot.utils.utils import cleanup_resources + #cleanup_resources(image_info) + logger_mp.info("Done!") + + +if __name__ == "__main__": + view_camera_main() diff --git a/additional_functions/wwwwdfv.py b/additional_functions/wwwwdfv.py new file mode 100644 index 000000000..47b30d5ea --- /dev/null +++ b/additional_functions/wwwwdfv.py @@ -0,0 +1,215 @@ +#!/usr/bin/env python3 +""" +Test script to switch between damp and zero torque mode every 2 seconds. +This script demonstrates switching between the safest robot modes. +""" + +import time +import sys +import signal + +# Add the unitree_sdk2_python path to sys.path +sys.path.append('/Users/nepyope/Documents/unitree/unitree_IL_lerobot/unitree_sdk2_python') + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient + + +class DampZeroTorqueTester: + def __init__(self): + # Initialize motion switcher client first + self.msc = MotionSwitcherClient() + self.msc.SetTimeout(5.0) + self.msc.Init() + + # Check current robot mode + print("Checking robot mode...") + status, result = self.msc.CheckMode() + if status == 0: + print(f"Current robot mode: {result.get('name', 'unknown')}") + if result.get('name'): + print("Releasing current mode...") + self.msc.ReleaseMode() + time.sleep(2.0) + else: + print(f"Could not check robot mode. Status: {status}") + + # Ensure robot is in 'ai' mode for locomotion commands + print("Setting robot to 'ai' mode...") + try: + self.msc.SelectMode("ai") + time.sleep(1.0) + print("Robot set to 'ai' mode successfully") + except Exception as e: + print(f"Warning: Could not set robot to 'ai' mode: {e}") + print("Trying to continue anyway...") + + # Initialize locomotion client + self.loco_client = LocoClient() + self.loco_client.SetTimeout(10.0) + self.loco_client.Init() + self.running = True + + # Set up signal handler for graceful shutdown + signal.signal(signal.SIGINT, self.signal_handler) + + def signal_handler(self, sig, frame): + """Handle Ctrl+C gracefully.""" + print("\n🛑 Received interrupt signal. Shutting down safely...") + self.running = False + # Release robot control + try: + self.msc.ReleaseMode() + print("🔓 Robot control released") + except: + pass + + def switch_to_damp(self): + """Switch robot to damping mode.""" + try: + print("🔒 Switching to DAMP mode (motors off)...") + self.loco_client.Damp() + return True + except Exception as e: + print(f"❌ Error switching to damp mode: {e}") + return False + + def switch_to_zero_torque(self): + """Switch robot to zero torque mode.""" + try: + print("⚡ Switching to ZERO TORQUE mode (motors on, no torque)...") + self.loco_client.ZeroTorque() + return True + except Exception as e: + print(f"❌ Error switching to zero torque mode: {e}") + return False + + def run_test(self): + """Run exactly 2 cycles: damp → zero_torque → damp → zero_torque.""" + print("=" * 60) + print("🤖 G1 Robot Damp/Zero Torque Mode Switcher") + print("=" * 60) + print("🔄 Running exactly 2 cycles:") + print(" Cycle 1: DAMP → ZERO TORQUE") + print(" Cycle 2: ZERO TORQUE → DAMP → ZERO TORQUE") + print(" Final mode: ZERO TORQUE") + print("\n⚠️ WARNING: Ensure robot is in a safe position!") + print("🛑 Press Ctrl+C to stop safely") + print("=" * 60) + + # Wait for user confirmation + input("Press Enter to start the test...") + + start_time = time.time() + + # Initial setup - start in damp mode + print(f"\n🚀 Starting test...") + if not self.switch_to_damp(): + print("❌ Failed to initialize damp mode. Aborting.") + return + + # Cycle 1: DAMP → ZERO TORQUE + print(f"\n📊 Cycle 1: DAMP → ZERO TORQUE") + print("⏳ Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_zero_torque(): + print("❌ Failed to switch to zero torque in cycle 1.") + return + + # Cycle 2: ZERO TORQUE → DAMP → ZERO TORQUE + print(f"\n📊 Cycle 2: ZERO TORQUE → DAMP → ZERO TORQUE") + print("⏳ Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_damp(): + print("❌ Failed to switch to damp in cycle 2.") + return + + print("⏳ Waiting 2 seconds...") + time.sleep(2) + + if not self.running: + return + + if not self.switch_to_zero_torque(): + print("❌ Failed to switch to zero torque in cycle 2.") + return + + # Test completed - robot ends in zero torque mode + print(f"\n🏁 Test completed after 2 cycles") + print("⚡ Final mode: ZERO TORQUE") + + # Release robot control to allow other programs to connect + print("🔓 Releasing robot control...") + try: + self.msc.ReleaseMode() + time.sleep(1.0) + # Release again to ensure complete cleanup + self.msc.ReleaseMode() + print("✅ Robot control released successfully") + except Exception as e: + print(f"⚠️ Warning: Could not release robot control: {e}") + + # Final check - ensure robot is in a free state + print("🔍 Final mode check...") + try: + status, result = self.msc.CheckMode() + if status == 0: + print(f"Final robot mode: {result.get('name', 'unknown')}") + if result.get('name'): + print("🔓 Performing final release...") + self.msc.ReleaseMode() + else: + print(f"Could not check final mode. Status: {status}") + except Exception as e: + print(f"Warning: Could not check final mode: {e}") + + print("\n✅ Test finished!") + print(f"📈 Total cycles: 2") + print(f"⏱️ Total time: {time.time() - start_time:.1f} seconds") + + +def main(): + print("G1 Robot Damp/Zero Torque Mode Switcher") + print("This script will run exactly 2 cycles ending in zero torque mode.") + + # Get network interface from command line or use default + if len(sys.argv) > 1: + network_interface = sys.argv[1] + print(f"Using network interface: {network_interface}") + else: + network_interface = "en7" # Default based on your setup + print(f"Using default network interface: {network_interface}") + + print("\nInitializing connection...") + + try: + # Initialize the channel factory + ChannelFactoryInitialize(0, network_interface) + print("✅ Channel factory initialized") + + # Create tester + tester = DampZeroTorqueTester() + print("✅ LocoClient created") + + # Run the test + tester.run_test() + + except Exception as e: + print(f"❌ Error during initialization: {e}") + print("\n🔧 Troubleshooting:") + print(" - Make sure you're connected to the robot's network") + print(" - Try: sudo ifconfig en7 192.168.123.100 netmask 255.255.255.0") + print(" - Verify robot is powered on and accessible") + print(" - Check if robot is in 'ai' mode") + + +if __name__ == "__main__": + main() diff --git a/eval_robot/assets/brainco_hand/brainco.yml b/eval_robot/assets/brainco_hand/brainco.yml new file mode 100644 index 000000000..59ae66d6a --- /dev/null +++ b/eval_robot/assets/brainco_hand/brainco.yml @@ -0,0 +1,61 @@ +left: + type: DexPilot # or vector + urdf_path: brainco_hand/brainco_left.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + "left_thumb_metacarpal_joint", + "left_thumb_proximal_joint", + "left_index_proximal_joint", + "left_middle_proximal_joint", + "left_ring_proximal_joint", + "left_pinky_proximal_joint", + ] + + # for DexPilot type + wrist_link_name: "base_link" + finger_tip_link_names: [ "left_thumb_tip", "left_index_tip", "left_middle_tip", "left_ring_tip", "left_pinky_tip" ] + # If you do not know exactly how it is used, please leave it to None for default. + target_link_human_indices_dexpilot: [[ 9, 14, 19, 24, 14, 19, 24, 19, 24, 24, 0, 0, 0, 0, 0], [ 4, 4, 4, 4, 9, 9, 9, 14, 14, 19, 4, 9, 14, 19, 24]] + + # for vector type + target_origin_link_names: ["base_link", "base_link", "base_link", "base_link", "base_link"] + target_task_link_names: [ "left_thumb_tip", "left_index_tip", "left_middle_tip", "left_ring_tip", "left_pinky_tip" ] + target_link_human_indices_vector: [ [ 0, 0, 0, 0, 0 ], [ 4, 9, 14, 19, 24 ] ] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times larger than normal human hand, then this scaling factor should be 1.6 + scaling_factor: 0.90 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 + +right: + type: DexPilot # or vector + urdf_path: brainco_hand/brainco_right.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + "right_thumb_metacarpal_joint", + "right_thumb_proximal_joint", + "right_index_proximal_joint", + "right_middle_proximal_joint", + "right_ring_proximal_joint", + "right_pinky_proximal_joint", + ] + # for DexPilot type + wrist_link_name: "base_link" + finger_tip_link_names: [ "right_thumb_tip", "right_index_tip", "right_middle_tip", "right_ring_tip", "right_pinky_tip" ] + target_link_human_indices_dexpilot: [[ 9, 14, 19, 24, 14, 19, 24, 19, 24, 24, 0, 0, 0, 0, 0], [ 4, 4, 4, 4, 9, 9, 9, 14, 14, 19, 4, 9, 14, 19, 24]] + + # for vector type + target_origin_link_names: ["base_link", "base_link", "base_link", "base_link", "base_link"] + target_task_link_names: [ "right_thumb_tip", "right_index_tip", "right_middle_tip", "right_ring_tip", "right_pinky_tip" ] + target_link_human_indices_vector: [ [ 0, 0, 0, 0, 0 ], [ 4, 9, 14, 19, 24 ] ] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times larger than normal human hand, then this scaling factor should be 1.6 + scaling_factor: 0.90 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 diff --git a/eval_robot/assets/brainco_hand/meshes/left_base_link.STL b/eval_robot/assets/brainco_hand/meshes/left_base_link.STL new file mode 100644 index 000000000..f8aee1028 Binary files /dev/null and b/eval_robot/assets/brainco_hand/meshes/left_base_link.STL differ diff --git a/eval_robot/assets/brainco_hand/meshes/left_index_distal_Link.STL b/eval_robot/assets/brainco_hand/meshes/left_index_distal_Link.STL new file mode 100644 index 000000000..89d3a1b51 Binary files /dev/null and b/eval_robot/assets/brainco_hand/meshes/left_index_distal_Link.STL differ diff --git a/eval_robot/assets/brainco_hand/meshes/left_index_proximal_Link.STL 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robot description (URDF & MJCF) for the [Unitree G1](https://www.unitree.com/g1/), developed by [Unitree Robotics](https://www.unitree.com/). + +MJCF/URDF for the G1 robot: + +| MJCF/URDF file name | `mode_machine` | Hip roll reduction ratio | Update status | dof#leg | dof#waist | dof#arm | dof#hand | +| ----------------------------- | :------------: | :----------------------: | ------------- | :-----: | :-------: | :-----: | :------: | +| `g1_23dof` | 1 | 14.5 | Beta | 6*2 | 1 | 5*2 | 0 | +| `g1_29dof` | 2 | 14.5 | Beta | 6*2 | 3 | 7*2 | 0 | +| `g1_29dof_with_hand` | 2 | 14.5 | Beta | 6*2 | 3 | 7*2 | 7*2 | +| `g1_29dof_lock_waist` | 3 | 14.5 | Beta | 6*2 | 1 | 7*2 | 0 | +| `g1_23dof_rev_1_0` | 4 | 22.5 | Up-to-date | 6*2 | 1 | 5*2 | 0 | +| `g1_29dof_rev_1_0` | 5 | 22.5 | Up-to-date | 6*2 | 3 | 7*2 | 0 | +| `g1_29dof_with_hand_rev_1_0` | 5 | 22.5 | Up-to-date | 6*2 | 3 | 7*2 | 7*2 | +| `g1_29dof_lock_waist_rev_1_0` | 6 | 22.5 | Up-to-date | 6*2 | 1 | 7*2 | 0 | +| `g1_dual_arm` | 9 | null | Up-to-date | 0 | 0 | 7*2 | 0 | + +## Visulization with [MuJoCo](https://github.com/google-deepmind/mujoco) + +1. Open MuJoCo Viewer + + ```bash + pip install mujoco + python -m mujoco.viewer + ``` + +2. 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how it is used, please leave it to None for default. + target_link_human_indices_dexpilot: [[ 9, 14, 19, 24, 14, 19, 24, 19, 24, 24, 0, 0, 0, 0, 0], [ 4, 4, 4, 4, 9, 9, 9, 14, 14, 19, 4, 9, 14, 19, 24]] + + # for vector type + target_origin_link_names: [ "L_hand_base_link", "L_hand_base_link", "L_hand_base_link", "L_hand_base_link", "L_hand_base_link"] + target_task_link_names: [ "L_thumb_tip", "L_index_tip", "L_middle_tip", "L_ring_tip", "L_pinky_tip" ] + target_link_human_indices_vector: [ [ 0, 0, 0, 0, 0 ], [ 4, 9, 14, 19, 24 ] ] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times larger than normal human hand, then this scaling factor should be 1.6 + scaling_factor: 1.20 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 + +right: + type: DexPilot # or vector + urdf_path: inspire_hand/inspire_hand_right.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + 'R_thumb_proximal_yaw_joint', + 'R_thumb_proximal_pitch_joint', + 'R_index_proximal_joint', + 'R_middle_proximal_joint', + 'R_ring_proximal_joint', + 'R_pinky_proximal_joint' + ] + + # for DexPilot type + wrist_link_name: "R_hand_base_link" + finger_tip_link_names: [ "R_thumb_tip", "R_index_tip", "R_middle_tip", "R_ring_tip", "R_pinky_tip" ] + # If you do not know exactly how it is used, please leave it to None for + target_link_human_indices_dexpilot: [[ 9, 14, 19, 24, 14, 19, 24, 19, 24, 24, 0, 0, 0, 0, 0], [ 4, 4, 4, 4, 9, 9, 9, 14, 14, 19, 4, 9, 14, 19, 24]] + + target_origin_link_names: [ "R_hand_base_link", "R_hand_base_link", "R_hand_base_link", "R_hand_base_link", "R_hand_base_link"] + target_task_link_names: [ "R_thumb_tip", "R_index_tip", "R_middle_tip", "R_ring_tip", "R_pinky_tip" ] + target_link_human_indices_vector: [ [ 0, 0, 0, 0, 0 ], [ 4, 9, 14, 19, 24 ] ] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times 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urdf_path: unitree_hand/unitree_dex3_left.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + "left_hand_thumb_0_joint", + "left_hand_thumb_1_joint", + "left_hand_thumb_2_joint", + "left_hand_middle_0_joint", + "left_hand_middle_1_joint", + "left_hand_index_0_joint", + "left_hand_index_1_joint", + ] + + # for DexPilot type + wrist_link_name: "base_link" + finger_tip_link_names: [ "thumb_tip", "index_tip", "middle_tip"] + # If you do not know exactly how it is used, please leave it to None for default. + target_link_human_indices_dexpilot: [[ 9, 14, 14, 0, 0, 0], [ 4, 4, 9, 4, 9, 14]] + + # for vector type + target_origin_link_names: ["base_link_thumb","base_link_index","base_link_middle"] + target_task_link_names: ["thumb_tip","index_tip","middle_tip"] + target_link_human_indices_vector: [[0, 0, 0], [4, 9, 14]] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times larger than normal human hand, then this scaling factor should be 1.6 + scaling_factor: 1.0 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 + +right: + type: DexPilot # or vector + urdf_path: unitree_hand/unitree_dex3_right.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + "right_hand_thumb_0_joint", + "right_hand_thumb_1_joint", + "right_hand_thumb_2_joint", + "right_hand_index_0_joint", + "right_hand_index_1_joint", + "right_hand_middle_0_joint", + "right_hand_middle_1_joint", + ] + # for DexPilot type + wrist_link_name: "base_link" + finger_tip_link_names: [ "thumb_tip", "index_tip", "middle_tip"] + target_link_human_indices_dexpilot: [[ 9, 14, 14, 0, 0, 0], [ 4, 4, 9, 4, 9, 14]] + + # for vector type + target_origin_link_names: ["base_link_thumb","base_link_index","base_link_middle"] + target_task_link_names: ["thumb_tip", "index_tip", "middle_tip"] + target_link_human_indices_vector: [[0, 0, 0], [4, 9, 14]] + + # Scaling factor for vector retargeting only + # For example, Allegro is 1.6 times larger than normal human hand, then this scaling factor should be 1.6 + scaling_factor: 1.0 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 diff --git a/eval_robot/bimanual_teleop.py b/eval_robot/bimanual_teleop.py new file mode 100644 index 000000000..697c5d57d --- /dev/null +++ b/eval_robot/bimanual_teleop.py @@ -0,0 +1,316 @@ +# teleop_to_robot_bimanual_with_cams_and_pedal.py +import os +import re +import time +import threading +import traceback +import numpy as np +import cv2 + +from evdev import InputDevice, categorize, ecodes + +from unitree_lerobot.lerobot.src.lerobot.teleoperators.homunculus import HomunculusArm, HomunculusArmConfig +from unitree_lerobot.eval_robot.robot_control.robot_arm_test import G1_29_ArmController, G1_29_JointIndex +from unitree_lerobot.eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK +from unitree_lerobot.eval_robot.make_robot import setup_image_client + +# ------------ Config ------------ +PEDAL_DEV = "/dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd" +EPISODE_ROOT = "box_task" +DISPLAY_SCALE_HEAD = (1280, 960) # ~2x per eye when binocular +DISPLAY_SCALE_WRIST = (960, 720) # ~2x per wrist view +LOG_HZ = 30.0 +CONTROL_HZ = 100.0 +# -------------------------------- + +# LEFT ARM order: S_pitch, S_yaw, S_roll, Elbow_flex, Wrist_roll +def scale_to_joint_limits_left(u5: np.ndarray) -> np.ndarray: + mins = np.array([-3.05, 0.00, -2.30, -1.00, 1.95], dtype=np.float32) + maxs = np.array([ 1.65, 1.20, 1.30, 1.00, -1.00], dtype=np.float32) + u = np.clip(u5.astype(np.float32), -1.0, 1.0) + return mins + (u + 1.0) * 0.5 * (maxs - mins) + +# RIGHT ARM order: S_pitch, S_yaw, S_roll, Elbow_flex, Wrist_roll +def scale_to_joint_limits_right(u5: np.ndarray) -> np.ndarray: + # (min > max for some joints is intentional to mirror orientation) + mins = np.array([-3.05, 0.00, 2.30, 2.00, -1.95], dtype=np.float32) + maxs = np.array([ 1.65, -1.20, -1.30, -1.00, 1.00], dtype=np.float32) + u = np.clip(u5.astype(np.float32), -1.0, 1.0) + return mins + (u + 1.0) * 0.5 * (maxs - mins) + +# ---- Episode numbering helpers ---- +_num_re = re.compile(r"^\d+$") + +def _ensure_root(): + os.makedirs(EPISODE_ROOT, exist_ok=True) + +def _next_episode_id() -> int: + _ensure_root() + existing = [int(d) for d in os.listdir(EPISODE_ROOT) + if _num_re.match(d) and os.path.isdir(os.path.join(EPISODE_ROOT, d))] + return (max(existing) + 1) if existing else 1 + +def _episode_dir(ep_id: int) -> str: + d = os.path.join(EPISODE_ROOT, str(ep_id)) + os.makedirs(d, exist_ok=True) + return d + +# ---- Pedal listener (runs in a thread) ---- +class PedalState: + def __init__(self): + self.recording = False + self.lock = threading.Lock() + self.start_trigger = False + self.stop_trigger = False + + def arm_start(self): + with self.lock: + self.start_trigger = True + + def arm_stop(self): + with self.lock: + self.stop_trigger = True + + def consume_triggers(self): + with self.lock: + s, t = self.start_trigger, self.stop_trigger + self.start_trigger = False + self.stop_trigger = False + return s, t + +def pedal_thread(dev_path: str, pstate: PedalState): + try: + dev = InputDevice(dev_path) + print(f"[Pedal] Using {dev.path} ({dev.name})") + for ev in dev.read_loop(): + if ev.type != ecodes.EV_KEY: + continue + key = categorize(ev) # key.keystate: 1=down, 0=up, 2=hold + code = getattr(key, "keycode", None) + state = getattr(key, "keystate", None) + if code == "KEY_A" and state == 1: + pstate.arm_start() + elif code == "KEY_B" and state == 1: + pstate.arm_stop() + except PermissionError as e: + print(f"[Pedal] Permission error opening {dev_path}: {e}") + print(" Try: sudo setfacl -m u:$USER:rw ") + except Exception as e: + print(f"[Pedal] Error: {e}") + traceback.print_exc() + +def main(): + # --- Teleop (Homunculus) --- + left_exo = HomunculusArm(HomunculusArmConfig("/dev/ttyACM0", id="unitree_left")) + right_exo = HomunculusArm(HomunculusArmConfig("/dev/ttyACM1", id="unitree_right")) + left_exo.connect(calibrate=True) + right_exo.connect(calibrate=True) + + # --- Robot --- + arm_ik = G1_29_ArmIK() + arm_ctrl = G1_29_ArmController(motion_mode=False, simulation_mode=False) + + # Zero non-arm joints ONCE + arm_joint_indices = set(range(15, 29)) # 15..28 are arms + for jid in G1_29_JointIndex: + if jid.value not in arm_joint_indices: + arm_ctrl.msg.motor_cmd[jid].mode = 1 + arm_ctrl.msg.motor_cmd[jid].q = 0.0 + arm_ctrl.msg.motor_cmd[jid].dq = 0.0 + arm_ctrl.msg.motor_cmd[jid].tau = 0.0 + + # --- Camera client --- + class _Cfg: + sim = False + arm = "G1_29" + ee = "dex3" + motion = False + image_info = setup_image_client(_Cfg) + tv_img_array = image_info["tv_img_array"] + wrist_img_array = image_info["wrist_img_array"] + is_binocular = image_info["is_binocular"] + has_wrist_cam = image_info["has_wrist_cam"] + + # --- Pedal listener thread --- + pstate = PedalState() + th = threading.Thread(target=pedal_thread, args=(PEDAL_DEV, pstate), daemon=True) + th.start() + + # --- Logging state --- + frames_rgb = [] # current episode frames (N, 640, 480, 3) RGB + obs_states = [] # current episode obs (N, 14) + recording = False + ep_id = None + log_dt = 1.0 / LOG_HZ + next_log_t = time.perf_counter() + log_dt + + try: + dt = 1.0 / CONTROL_HZ + k = 0 + + while True: + t0 = time.perf_counter() + + # Handle pedal triggers + start_trig, stop_trig = pstate.consume_triggers() + if start_trig and not recording: + ep_id = _next_episode_id() + _episode_dir(ep_id) + frames_rgb = [] + obs_states = [] + recording = True + print(f"[Episode] START -> {ep_id}") + + if stop_trig and recording: + # Save episode + try: + ep_dir = _episode_dir(ep_id) + if len(frames_rgb) > 0: + frames_np = np.stack(frames_rgb, axis=0) + np.savez_compressed(os.path.join(ep_dir, "frames_rgb_640x480.npz"), frames_np) + print(f"[Episode] Saved {frames_np.shape} frames to {ep_dir}/frames_rgb_640x480.npz") + if len(obs_states) > 0: + obs_np = np.stack(obs_states, axis=0).astype(np.float32) + np.savez_compressed(os.path.join(ep_dir, "obs_state.npz"), obs_np) + print(f"[Episode] Saved {obs_np.shape} obs to {ep_dir}/obs_state.npz") + except Exception: + traceback.print_exc() + finally: + frames_rgb = [] + obs_states = [] + recording = False + print(f"[Episode] STOP -> {ep_id}") + ep_id = None + + # --- LEFT teleop + teleop_l = left_exo.get_action() + vals_l = list(teleop_l.values())[:5] + vals_l = [vals_l[0]] + [-v for v in vals_l[1:]] + norm_l = np.asarray(vals_l, dtype=np.float32) / 100.0 + q_left = scale_to_joint_limits_left(norm_l) + + # --- RIGHT teleop + teleop_r = right_exo.get_action() + vals_r = list(teleop_r.values())[:5] + vals_r = [vals_r[0]] + [-v for v in vals_r[1:]] + norm_r = np.asarray(vals_r, dtype=np.float32) / 100.0 + q_right = scale_to_joint_limits_right(norm_r) + + # Build combined command (left: 0..4, right: 7..11) + arm_cmd = np.zeros(14, dtype=np.float32) + arm_cmd[0:5] = q_left + arm_cmd[7:12] = q_right + + # Send + tau = arm_ik.solve_tau(arm_cmd) + arm_ctrl.ctrl_dual_arm(arm_cmd, tau) + + # Readback for display/log + q_curr = None + try: + q_curr = arm_ctrl.get_current_dual_arm_q() # (14,) + if (k % 30) == 0: + qL = q_curr[0:5] + qR = q_curr[7:12] + print(f"[k={k:05d}] q_left={np.array2string(qL, precision=3)} q_right={np.array2string(qR, precision=3)}") + except Exception: + if (k % 300) == 0: + traceback.print_exc() + + # Camera display (bigger windows) + head_image = tv_img_array.copy() + if head_image is not None and head_image.size > 0 and np.any(head_image): + if is_binocular: + h, w = head_image.shape[:2] + left_head = head_image[:, :w // 2] + right_head = head_image[:, w // 2:] + cv2.imshow("Head Left", cv2.resize(left_head, DISPLAY_SCALE_HEAD)) + cv2.imshow("Head Right", cv2.resize(right_head, DISPLAY_SCALE_HEAD)) + else: + h, w = head_image.shape[:2] + # double-ish + cv2.imshow("Head", cv2.resize(head_image, (max(2*w // 1, 1), max(2*h // 1, 1)))) + + if has_wrist_cam and wrist_img_array is not None: + wrist_image = wrist_img_array.copy() + if wrist_image is not None and wrist_image.size > 0 and np.any(wrist_image): + h, w = wrist_image.shape[:2] + left_wrist = wrist_image[:, :w // 2] + right_wrist = wrist_image[:, w // 2:] + cv2.imshow("Wrist Left", cv2.resize(left_wrist, DISPLAY_SCALE_WRIST)) + cv2.imshow("Wrist Right", cv2.resize(right_wrist, DISPLAY_SCALE_WRIST)) + + # 30 Hz logging only when recording + now = time.perf_counter() + if recording and now >= next_log_t: + # Frame: pick head-left (or single head), convert BGR->RGB, resize 640x480 + if head_image is not None and head_image.size > 0 and np.any(head_image): + if is_binocular: + h, w = head_image.shape[:2] + src = head_image[:, :w // 2] # left eye + else: + src = head_image + frame_640x480 = cv2.resize(src, (640, 480)) + frame_rgb = cv2.cvtColor(frame_640x480, cv2.COLOR_BGR2RGB) + frames_rgb.append(frame_rgb) + + # Obs + if q_curr is not None: + obs_states.append(np.asarray(q_curr, dtype=np.float32)) + + # schedule next tick (keeps cadence stable even if we miss one) + next_log_t += (1.0 / LOG_HZ) + + # UI + key = cv2.waitKey(1) & 0xFF + if key == ord('q'): + print("Quit command received.") + break + elif key == ord('s'): + ts = time.strftime("%Y%m%d_%H%M%S") + if head_image is not None and head_image.size > 0: + cv2.imwrite(f"head_{ts}.jpg", head_image) + print(f"Saved head_{ts}.jpg") + + k += 1 + + # Loop pacing + elapsed = time.perf_counter() - t0 + if elapsed < dt: + time.sleep(dt - elapsed) + + except KeyboardInterrupt: + pass + except Exception: + traceback.print_exc() + finally: + # If recording when quitting, finalize the episode + if recording and ep_id is not None: + try: + ep_dir = _episode_dir(ep_id) + if len(frames_rgb) > 0: + frames_np = np.stack(frames_rgb, axis=0) + np.savez_compressed(os.path.join(ep_dir, "frames_rgb_640x480.npz"), frames_np) + print(f"[Episode] Saved {frames_np.shape} frames to {ep_dir}/frames_rgb_640x480.npz") + if len(obs_states) > 0: + obs_np = np.stack(obs_states, axis=0).astype(np.float32) + np.savez_compressed(os.path.join(ep_dir, "obs_state.npz"), obs_np) + print(f"[Episode] Saved {obs_np.shape} obs to {ep_dir}/obs_state.npz") + except Exception: + traceback.print_exc() + + # Cleanup OpenCV and shared resources + cv2.destroyAllWindows() + try: + from unitree_lerobot.eval_robot.utils.utils import cleanup_resources + cleanup_resources(image_info) + except Exception: + pass + for exo in (left_exo, right_exo): + try: + exo.disconnect() + except Exception: + pass + +if __name__ == "__main__": + main() diff --git a/eval_robot/eval_g1.py b/eval_robot/eval_g1.py new file mode 100644 index 000000000..f63b9fbed --- /dev/null +++ b/eval_robot/eval_g1.py @@ -0,0 +1,190 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import torch +import logging + +import numpy as np +from pprint import pformat +from dataclasses import asdict +from torch import nn +from contextlib import nullcontext + +from lerobot.policies.factory import make_policy +from lerobot.utils.utils import ( + get_safe_torch_device, + init_logging, +) +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from multiprocessing.sharedctypes import SynchronizedArray + +from unitree_lerobot.eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from unitree_lerobot.eval_robot.utils.utils import ( + cleanup_resources, + predict_action, + to_list, + to_scalar, + EvalRealConfig, +) +from unitree_lerobot.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def eval_policy( + cfg: EvalRealConfig, + policy: torch.nn.Module, + dataset: LeRobotDataset, +): + assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module." + + logger_mp.info(f"Arguments: {cfg}") + + if cfg.visualization: + rerun_logger = RerunLogger() + + policy.reset() # Set policy to evaluation mode + + image_info = None + try: + # --- Setup Phase --- + image_info = setup_image_client(cfg) + robot_interface = setup_robot_interface(cfg) + + # Unpack interfaces for convenience + arm_ctrl, arm_ik, ee_shared_mem, arm_dof, ee_dof = ( + robot_interface[key] for key in ["arm_ctrl", "arm_ik", "ee_shared_mem", "arm_dof", "ee_dof"] + ) + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam = ( + image_info[key] + for key in [ + "tv_img_array", + "wrist_img_array", + "tv_img_shape", + "wrist_img_shape", + "is_binocular", + "has_wrist_cam", + ] + ) + + # Get initial pose from the first step of the dataset + episode_idx = 0 + episode_info = dataset.meta.episodes[episode_idx] + + from_idx = episode_info["dataset_from_index"] + to_idx = episode_info["dataset_to_index"] + step = dataset[from_idx] + init_arm_pose = step["observation.state"][:arm_dof].cpu().numpy() + + user_input = input("Enter 's' to initialize the robot and start the evaluation: ") + idx = 0 + print(f"user_input: {user_input}") + full_state = None + if user_input.lower() == "s": + # "The initial positions of the robot's arm and fingers take the initial positions during data recording." + logger_mp.info("Initializing robot to starting pose...") + tau = robot_interface["arm_ik"].solve_tau(init_arm_pose) + robot_interface["arm_ctrl"].ctrl_dual_arm(init_arm_pose, tau) + time.sleep(1.0) # Give time for the robot to move + # --- Run Main Loop --- + logger_mp.info(f"Starting evaluation loop at {cfg.frequency} Hz.") + while True: + loop_start_time = time.perf_counter() + # 1. Get Observations + observation, current_arm_q = process_images_and_observations( + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam, arm_ctrl + ) + #convert wrist obs to tensors + observation["observation.images.cam_left_wrist"] = torch.from_numpy(observation["observation.images.cam_left_wrist"]) + observation["observation.images.cam_right_wrist"] = torch.from_numpy(observation["observation.images.cam_right_wrist"]) + left_ee_state = right_ee_state = np.array([]) + #if cfg.ee: + # with ee_shared_mem["lock"]: + # full_state = np.array(ee_shared_mem["state"][:]) + # left_ee_state = full_state[:ee_dof] + # right_ee_state = full_state[ee_dof:] + #pad with zeros + #left_ee_state = np.zeros(ee_dof) + #right_ee_state = np.zeros(ee_dof) + state_tensor = torch.from_numpy( + np.concatenate((current_arm_q, left_ee_state, right_ee_state), axis=0) + ).float() + observation["observation.state"] = state_tensor + # 2. Get Action from Policy + action = predict_action( + observation, + policy, + get_safe_torch_device(policy.config.device), + policy.config.use_amp, + step["task"], + use_dataset=cfg.use_dataset, + ) + action_np = action.cpu().numpy() + # 3. Execute Action + arm_action = action_np[:arm_dof]*0.1 + tau = arm_ik.solve_tau(arm_action) + arm_ctrl.ctrl_dual_arm(arm_action, tau) + + # if cfg.ee: + # ee_action_start_idx = arm_dof + # left_ee_action = action_np[ee_action_start_idx : ee_action_start_idx + ee_dof] + # right_ee_action = action_np[ee_action_start_idx + ee_dof : ee_action_start_idx + 2 * ee_dof] + # # logger_mp.info(f"EE Action: left {left_ee_action}, right {right_ee_action}") + + # if isinstance(ee_shared_mem["left"], SynchronizedArray): + # ee_shared_mem["left"][:] = to_list(left_ee_action) + # ee_shared_mem["right"][:] = to_list(right_ee_action) + # elif hasattr(ee_shared_mem["left"], "value") and hasattr(ee_shared_mem["right"], "value"): + # ee_shared_mem["left"].value = to_scalar(left_ee_action) + # ee_shared_mem["right"].value = to_scalar(right_ee_action) + + if cfg.visualization: + visualization_data(idx, observation, state_tensor.numpy(), action_np, rerun_logger) + idx += 1 + # Maintain frequency + time.sleep(max(0, (1.0 / cfg.frequency) - (time.perf_counter() - loop_start_time))) + except Exception as e: + logger_mp.info(f"An error occurred: {e}") + finally: + if image_info: + cleanup_resources(image_info) + + +@parser.wrap() +def eval_main(cfg: EvalRealConfig): + logging.info(pformat(asdict(cfg))) + + # Check device is available + device = get_safe_torch_device(cfg.policy.device, log=True) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + logging.info("Making policy.") + + dataset = LeRobotDataset(repo_id=cfg.repo_id) + + policy = make_policy(cfg=cfg.policy, ds_meta=dataset.meta) + policy.eval() + + with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(): + eval_policy(cfg=cfg, policy=policy, dataset=dataset) + + logging.info("End of eval") + + +if __name__ == "__main__": + init_logging() + eval_main() diff --git a/eval_robot/eval_g1_dataset.py b/eval_robot/eval_g1_dataset.py new file mode 100644 index 000000000..94abdd4e8 --- /dev/null +++ b/eval_robot/eval_g1_dataset.py @@ -0,0 +1,182 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import torch +import tqdm +import logging +import time +import numpy as np +import matplotlib.pyplot as plt +from pprint import pformat +from dataclasses import asdict +from torch import nn +from contextlib import nullcontext +from lerobot.policies.factory import make_policy +from lerobot.utils.utils import ( + get_safe_torch_device, + init_logging, +) +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from multiprocessing.sharedctypes import SynchronizedArray + +from unitree_lerobot.eval_robot.utils.utils import ( + extract_observation, + predict_action, + to_list, + to_scalar, + EvalRealConfig, +) +from unitree_lerobot.eval_robot.make_robot import setup_robot_interface +from unitree_lerobot.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data + + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def eval_policy( + cfg: EvalRealConfig, + policy: torch.nn.Module, + dataset: LeRobotDataset, +): + assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module." + + logger_mp.info(f"Arguments: {cfg}") + + if cfg.visualization: + rerun_logger = RerunLogger() + + policy.reset() # Set policy to evaluation mode + + # init pose + from_idx = dataset.episode_data_index["from"][0].item() + step = dataset[from_idx] + to_idx = dataset.episode_data_index["to"][0].item() + + ground_truth_actions = [] + predicted_actions = [] + + if cfg.send_real_robot: + robot_interface = setup_robot_interface(cfg) + arm_ctrl, arm_ik, ee_shared_mem, arm_dof, ee_dof = ( + robot_interface[key] for key in ["arm_ctrl", "arm_ik", "ee_shared_mem", "arm_dof", "ee_dof"] + ) + init_arm_pose = step["observation.state"][:arm_dof].cpu().numpy() + + # ===============init robot===================== + user_input = input("Please enter the start signal (enter 's' to start the subsequent program):") + if user_input.lower() == "s": + if cfg.send_real_robot: + # Initialize robot to starting pose + logger_mp.info("Initializing robot to starting pose...") + tau = robot_interface["arm_ik"].solve_tau(init_arm_pose) + robot_interface["arm_ctrl"].ctrl_dual_arm(init_arm_pose, tau) + + time.sleep(1) + + for step_idx in tqdm.tqdm(range(from_idx, to_idx)): + loop_start_time = time.perf_counter() + + step = dataset[step_idx] + observation = extract_observation(step) + + action = predict_action( + observation, + policy, + get_safe_torch_device(policy.config.device), + policy.config.use_amp, + step["task"], + use_dataset=True, + ) + action_np = action.cpu().numpy() + + ground_truth_actions.append(step["action"].numpy()) + predicted_actions.append(action_np) + + if cfg.send_real_robot: + # Execute Action + arm_action = action_np[:arm_dof] + tau = arm_ik.solve_tau(arm_action) + arm_ctrl.ctrl_dual_arm(arm_action, tau) + # logger_mp.info(f"Arm Action: {arm_action}") + + if cfg.ee: + ee_action_start_idx = arm_dof + left_ee_action = action_np[ee_action_start_idx : ee_action_start_idx + ee_dof] + right_ee_action = action_np[ee_action_start_idx + ee_dof : ee_action_start_idx + 2 * ee_dof] + # logger_mp.info(f"EE Action: left {left_ee_action}, right {right_ee_action}") + + if isinstance(ee_shared_mem["left"], SynchronizedArray): + ee_shared_mem["left"][:] = to_list(left_ee_action) + ee_shared_mem["right"][:] = to_list(right_ee_action) + elif hasattr(ee_shared_mem["left"], "value") and hasattr(ee_shared_mem["right"], "value"): + ee_shared_mem["left"].value = to_scalar(left_ee_action) + ee_shared_mem["right"].value = to_scalar(right_ee_action) + + if cfg.visualization: + visualization_data(step_idx, observation, observation["observation.state"], action_np, rerun_logger) + + # Maintain frequency + time.sleep(max(0, (1.0 / cfg.frequency) - (time.perf_counter() - loop_start_time))) + + ground_truth_actions = np.array(ground_truth_actions) + predicted_actions = np.array(predicted_actions) + + # Get the number of timesteps and action dimensions + n_timesteps, n_dims = ground_truth_actions.shape + + # Create a figure with subplots for each action dimension + fig, axes = plt.subplots(n_dims, 1, figsize=(12, 4 * n_dims), sharex=True) + fig.suptitle("Ground Truth vs Predicted Actions") + + # Plot each dimension + for i in range(n_dims): + ax = axes[i] if n_dims > 1 else axes + + ax.plot(ground_truth_actions[:, i], label="Ground Truth", color="blue") + ax.plot(predicted_actions[:, i], label="Predicted", color="red", linestyle="--") + ax.set_ylabel(f"Dim {i + 1}") + ax.legend() + + # Set common x-label + axes[-1].set_xlabel("Timestep") + + plt.tight_layout() + # plt.show() + + time.sleep(1) + plt.savefig("figure.png") + + +@parser.wrap() +def eval_main(cfg: EvalRealConfig): + logging.info(pformat(asdict(cfg))) + + # Check device is available + device = get_safe_torch_device(cfg.policy.device, log=True) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + logging.info("Making policy.") + + dataset = LeRobotDataset(repo_id=cfg.repo_id) + + policy = make_policy(cfg=cfg.policy, ds_meta=dataset.meta) + policy.eval() + + with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(): + eval_policy(cfg, policy, dataset) + + logging.info("End of eval") + + +if __name__ == "__main__": + init_logging() + eval_main() diff --git a/eval_robot/eval_g1_sim.py b/eval_robot/eval_g1_sim.py new file mode 100644 index 000000000..4d30e7650 --- /dev/null +++ b/eval_robot/eval_g1_sim.py @@ -0,0 +1,259 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import torch +import logging +import sys +import os + +import numpy as np + +# Suppress DDS debug output +class SuppressDDSOutput: + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + + +from pprint import pformat +from dataclasses import asdict +from torch import nn +from contextlib import nullcontext + +from lerobot.policies.factory import make_policy +from lerobot.utils.utils import ( + get_safe_torch_device, + init_logging, +) +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from multiprocessing.sharedctypes import SynchronizedArray + +from unitree_lerobot.eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from unitree_lerobot.eval_robot.utils.utils import ( + cleanup_resources, + predict_action, + to_list, + to_scalar, +) +from unitree_lerobot.eval_robot.utils.sim_savedata_utils import ( + EvalRealConfig, + process_data_add, + is_success, +) +from unitree_lerobot.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def eval_policy( + cfg: EvalRealConfig, + policy: torch.nn.Module, + dataset: LeRobotDataset, +): + assert isinstance(policy, nn.Module), "Policy must be a PyTorch nn module." + + #logger_mp.info(f"Arguments: {cfg}") + + if cfg.visualization: + rerun_logger = RerunLogger() + + policy.reset() # Set policy to evaluation mode + + image_info = None + try: + # --- Setup Phase --- + image_info = setup_image_client(cfg) + + # Suppress DDS debug output during robot setup + robot_interface = setup_robot_interface(cfg) + # Unpack interfaces for convenience + ( + arm_ctrl, + arm_ik, + ee_shared_mem, + arm_dof, + ee_dof, + sim_state_subscriber, + sim_reward_subscriber, + episode_writer, + reset_pose_publisher, + ) = ( + robot_interface[key] + for key in [ + "arm_ctrl", + "arm_ik", + "ee_shared_mem", + "arm_dof", + "ee_dof", + "sim_state_subscriber", + "sim_reward_subscriber", + "episode_writer", + "reset_pose_publisher", + ] + ) + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam = ( + image_info[key] + for key in [ + "tv_img_array", + "wrist_img_array", + "tv_img_shape", + "wrist_img_shape", + "is_binocular", + "has_wrist_cam", + ] + ) + #is_binocular = True#add flag properly + + # Get initial pose from the first step of the dataset + from_idx = dataset.episode_data_index["from"][0].item() + step = dataset[from_idx] + init_arm_pose = step["observation.state"][:arm_dof].cpu().numpy() + + idx = 0 + full_state = None + + reward_stats = { + "reward_sum": 0.0, + "episode_num": 0.0, + } + + # "The initial positions of the robot's arm and fingers take the initial positions during data recording." + #logger_mp.info("Initializing robot to starting pose...") + tau = robot_interface["arm_ik"].solve_tau(init_arm_pose) + robot_interface["arm_ctrl"].ctrl_dual_arm(init_arm_pose, tau)#hits init pose just fine + time.sleep(1.0) # Give time for the robot to move + + # --- Run Main Loop --- + #logger_mp.info(f"Starting evaluation loop at {cfg.frequency} Hz.") + while True: + if cfg.save_data: + if reward_stats["episode_num"] == 0: + episode_writer.create_episode() + loop_start_time = time.perf_counter() + + # 1. Get Observations + observation, current_arm_q = process_images_and_observations( + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam, arm_ctrl + ) + + + left_ee_state = right_ee_state = np.array([]) + if cfg.ee: + with ee_shared_mem["lock"]: + full_state = np.array(ee_shared_mem["state"][:]) + left_ee_state = full_state[:ee_dof] + right_ee_state = full_state[ee_dof:] + state_tensor = torch.from_numpy( + np.concatenate((current_arm_q, left_ee_state, right_ee_state), axis=0) + ).float() + observation["observation.state"] = state_tensor + # 2. Get Action from Policy + #action_np = np.random.random(arm_dof) + action = predict_action( + observation, + policy, + get_safe_torch_device(policy.config.device), + policy.config.use_amp, + step["task"], + use_dataset=cfg.use_dataset, + ) + action_np = action.cpu().numpy() + # 3. Execute Action + arm_action = action_np[:arm_dof] + #arm action is random vector + + tau = arm_ik.solve_tau(arm_action) + + # Suppress DDS debug output during control commands + arm_ctrl.ctrl_dual_arm(arm_action, tau) + + if cfg.ee: + ee_action_start_idx = arm_dof + left_ee_action = action_np[ee_action_start_idx : ee_action_start_idx + ee_dof] + right_ee_action = action_np[ee_action_start_idx + ee_dof : ee_action_start_idx + 2 * ee_dof] + logger_mp.info(f"EE Action: left {left_ee_action}, right {right_ee_action}") + + if isinstance(ee_shared_mem["left"], SynchronizedArray): + ee_shared_mem["left"][:] = to_list(left_ee_action) + ee_shared_mem["right"][:] = to_list(right_ee_action) + elif hasattr(ee_shared_mem["left"], "value") and hasattr(ee_shared_mem["right"], "value"): + ee_shared_mem["left"].value = to_scalar(left_ee_action) + ee_shared_mem["right"].value = to_scalar(right_ee_action) + # save data + #if cfg.save_data: + # process_data_add(episode_writer, observation, current_arm_q, full_state, action, arm_dof, ee_dof) + + # is_success( + # sim_reward_subscriber, + # episode_writer, + # reset_pose_publisher, + # policy, + # cfg, + # reward_stats, + # init_arm_pose, + # robot_interface, + # ) + + if cfg.visualization: + visualization_data(idx, observation, state_tensor.numpy(), action_np, rerun_logger) + idx += 1 + reward_stats["episode_num"] = reward_stats["episode_num"] + 1 + # Maintain frequency + time.sleep(max(0, (1.0 / cfg.frequency) - (time.perf_counter() - loop_start_time))) + + except Exception as e: + logger_mp.info(f"An error occurred: {e}") + pass + finally: + if image_info: + cleanup_resources(image_info) + # Clean up sim state subscriber if it exists + if sim_state_subscriber and not getattr(sim_state_subscriber, "stopped", False): + sim_state_subscriber.stop_subscribe() + if sim_reward_subscriber and not getattr(sim_reward_subscriber, "stopped", False): + sim_reward_subscriber.stop_subscribe() + + +@parser.wrap() +def eval_main(cfg: EvalRealConfig): + logging.info(pformat(asdict(cfg))) + + # Check device is available + device = get_safe_torch_device(cfg.policy.device, log=True) + + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + + logging.info("Making policy.") + + dataset = LeRobotDataset(repo_id=cfg.repo_id) + + policy = make_policy(cfg=cfg.policy, ds_meta=dataset.meta) + policy.eval() + + with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(): + eval_policy(cfg=cfg, policy=policy, dataset=dataset) + + logging.info("End of eval") + + +if __name__ == "__main__": + init_logging() + eval_main() diff --git a/eval_robot/image_server/image_client.py b/eval_robot/image_server/image_client.py new file mode 100644 index 000000000..8beeed459 --- /dev/null +++ b/eval_robot/image_server/image_client.py @@ -0,0 +1,220 @@ +import cv2 +import zmq +import numpy as np +import time +import struct +from collections import deque +from multiprocessing import shared_memory + + +class ImageClient: + def __init__( + self, + tv_img_shape=None, + tv_img_shm_name=None, + wrist_img_shape=None, + wrist_img_shm_name=None, + image_show=False, + server_address="192.168.123.164", + port=5554, + Unit_Test=False, + ): + """ + tv_img_shape: User's expected head camera resolution shape (H, W, C). It should match the output of the image service terminal. + tv_img_shm_name: Shared memory is used to easily transfer images across processes to the Vuer. + wrist_img_shape: User's expected wrist camera resolution shape (H, W, C). It should maintain the same shape as tv_img_shape. + wrist_img_shm_name: Shared memory is used to easily transfer images. + image_show: Whether to display received images in real time. + server_address: The ip address to execute the image server script. + port: The port number to bind to. It should be the same as the image server. + Unit_Test: When both server and client are True, it can be used to test the image transfer latency, \ + network jitter, frame loss rate and other information. + """ + self.running = True + self._image_show = image_show + self._server_address = server_address + self._port = port + + self.tv_img_shape = tv_img_shape + self.wrist_img_shape = wrist_img_shape + + self.tv_enable_shm = False + if self.tv_img_shape is not None and tv_img_shm_name is not None: + self.tv_image_shm = shared_memory.SharedMemory(name=tv_img_shm_name) + self.tv_img_array = np.ndarray(tv_img_shape, dtype=np.uint8, buffer=self.tv_image_shm.buf) + self.tv_enable_shm = True + + self.wrist_enable_shm = False + if self.wrist_img_shape is not None and wrist_img_shm_name is not None: + self.wrist_image_shm = shared_memory.SharedMemory(name=wrist_img_shm_name) + self.wrist_img_array = np.ndarray(wrist_img_shape, dtype=np.uint8, buffer=self.wrist_image_shm.buf) + self.wrist_enable_shm = True + + # Performance evaluation parameters + self._enable_performance_eval = Unit_Test + if self._enable_performance_eval: + self._init_performance_metrics() + + def _init_performance_metrics(self): + self._frame_count = 0 # Total frames received + self._last_frame_id = -1 # Last received frame ID + + # Real-time FPS calculation using a time window + self._time_window = 1.0 # Time window size (in seconds) + self._frame_times = deque() # Timestamps of frames received within the time window + + # Data transmission quality metrics + self._latencies = deque() # Latencies of frames within the time window + self._lost_frames = 0 # Total lost frames + self._total_frames = 0 # Expected total frames based on frame IDs + + def cleanup(self): + """Clean up shared memory resources.""" + try: + if hasattr(self, 'tv_image_shm') and self.tv_image_shm: + self.tv_image_shm.close() + except Exception as e: + print(f"Warning: Failed to cleanup tv_image_shm: {e}") + + try: + if hasattr(self, 'wrist_image_shm') and self.wrist_image_shm: + self.wrist_image_shm.close() + except Exception as e: + print(f"Warning: Failed to cleanup wrist_image_shm: {e}") + + def __del__(self): + """Destructor""" + self.cleanup() + + def _update_performance_metrics(self, timestamp, frame_id, receive_time): + # Update latency + latency = receive_time - timestamp + self._latencies.append(latency) + + # Remove latencies outside the time window + while self._latencies and self._frame_times and self._latencies[0] < receive_time - self._time_window: + self._latencies.popleft() + + # Update frame times + self._frame_times.append(receive_time) + # Remove timestamps outside the time window + while self._frame_times and self._frame_times[0] < receive_time - self._time_window: + self._frame_times.popleft() + + # Update frame counts for lost frame calculation + expected_frame_id = self._last_frame_id + 1 if self._last_frame_id != -1 else frame_id + if frame_id != expected_frame_id: + lost = frame_id - expected_frame_id + if lost < 0: + print(f"[Image Client] Received out-of-order frame ID: {frame_id}") + else: + self._lost_frames += lost + print( + f"[Image Client] Detected lost frames: {lost}, Expected frame ID: {expected_frame_id}, Received frame ID: {frame_id}" + ) + self._last_frame_id = frame_id + self._total_frames = frame_id + 1 + + self._frame_count += 1 + + def _print_performance_metrics(self, receive_time): + if self._frame_count % 30 == 0: + # Calculate real-time FPS + real_time_fps = len(self._frame_times) / self._time_window if self._time_window > 0 else 0 + + # Calculate latency metrics + if self._latencies: + avg_latency = sum(self._latencies) / len(self._latencies) + max_latency = max(self._latencies) + min_latency = min(self._latencies) + jitter = max_latency - min_latency + else: + avg_latency = max_latency = min_latency = jitter = 0 + + # Calculate lost frame rate + lost_frame_rate = (self._lost_frames / self._total_frames) * 100 if self._total_frames > 0 else 0 + + print( + f"[Image Client] Real-time FPS: {real_time_fps:.2f}, Avg Latency: {avg_latency * 1000:.2f} ms, Max Latency: {max_latency * 1000:.2f} ms, \ + Min Latency: {min_latency * 1000:.2f} ms, Jitter: {jitter * 1000:.2f} ms, Lost Frame Rate: {lost_frame_rate:.2f}%" + ) + + def _close(self): + self._socket.close() + self._context.term() + if self._image_show: + cv2.destroyAllWindows() + print("Image client has been closed.") + + def receive_process(self): + # Set up ZeroMQ context and socket + self._context = zmq.Context() + self._socket = self._context.socket(zmq.SUB) + self._socket.connect(f"tcp://{self._server_address}:{self._port}") + self._socket.setsockopt_string(zmq.SUBSCRIBE, "") + + print("\nImage client has started, waiting to receive data...") + try: + while self.running: + # Receive message + message = self._socket.recv() + receive_time = time.time() + + if self._enable_performance_eval: + header_size = struct.calcsize("dI") + try: + # Attempt to extract header and image data + header = message[:header_size] + jpg_bytes = message[header_size:] + timestamp, frame_id = struct.unpack("dI", header) + except struct.error as e: + print(f"[Image Client] Error unpacking header: {e}, discarding message.") + continue + else: + # No header, entire message is image data + jpg_bytes = message + # Decode image + np_img = np.frombuffer(jpg_bytes, dtype=np.uint8) + current_image = cv2.imdecode(np_img, cv2.IMREAD_COLOR) + if current_image is None: + print("[Image Client] Failed to decode image.") + continue + + if self.tv_enable_shm: + np.copyto(self.tv_img_array, np.array(current_image[:, : self.tv_img_shape[1]])) + + if self.wrist_enable_shm: + np.copyto(self.wrist_img_array, np.array(current_image[:, -self.wrist_img_shape[1] :])) + + if self._image_show: + height, width = current_image.shape[:2] + resized_image = cv2.resize(current_image, (width // 2, height // 2)) + cv2.imshow("Image Client Stream", resized_image) + if cv2.waitKey(1) & 0xFF == ord("q"): + self.running = False + + if self._enable_performance_eval: + self._update_performance_metrics(timestamp, frame_id, receive_time) + self._print_performance_metrics(receive_time) + + except KeyboardInterrupt: + print("Image client interrupted by user.") + except Exception as e: + print(f"[Image Client] An error occurred while receiving data: {e}") + finally: + self._close() + + +if __name__ == "__main__": + # example1 + # tv_img_shape = (480, 1280, 3) + # img_shm = shared_memory.SharedMemory(create=True, size=np.prod(tv_img_shape) * np.uint8().itemsize) + # img_array = np.ndarray(tv_img_shape, dtype=np.uint8, buffer=img_shm.buf) + # img_client = ImageClient(tv_img_shape = tv_img_shape, tv_img_shm_name = img_shm.name) + # img_client.receive_process() + + # example2 + # Initialize the client with performance evaluation enabled + # client = ImageClient(image_show = True, server_address='127.0.0.1', Unit_Test=True) # local test + client = ImageClient(image_show=True, server_address="192.168.123.164", Unit_Test=False) # deployment test + client.receive_process() diff --git a/eval_robot/image_server/image_server.py b/eval_robot/image_server/image_server.py new file mode 100644 index 000000000..8fd5333e3 --- /dev/null +++ b/eval_robot/image_server/image_server.py @@ -0,0 +1,347 @@ +import cv2 +import zmq +import time +import struct +from collections import deque +import numpy as np +import pyrealsense2 as rs +import logging_mp + +logger_mp = logging_mp.get_logger(__name__, level=logging_mp.DEBUG) + + +class RealSenseCamera(object): + def __init__(self, img_shape, fps, serial_number=None, enable_depth=False) -> None: + """ + img_shape: [height, width] + serial_number: serial number + """ + self.img_shape = img_shape + self.fps = fps + self.serial_number = serial_number + self.enable_depth = enable_depth + + align_to = rs.stream.color + self.align = rs.align(align_to) + self.init_realsense() + + def init_realsense(self): + self.pipeline = rs.pipeline() + config = rs.config() + if self.serial_number is not None: + config.enable_device(self.serial_number) + + config.enable_stream(rs.stream.color, self.img_shape[1], self.img_shape[0], rs.format.bgr8, self.fps) + + if self.enable_depth: + config.enable_stream(rs.stream.depth, self.img_shape[1], self.img_shape[0], rs.format.z16, self.fps) + + profile = self.pipeline.start(config) + self._device = profile.get_device() + if self._device is None: + logger_mp.error("[Image Server] pipe_profile.get_device() is None .") + if self.enable_depth: + assert self._device is not None + depth_sensor = self._device.first_depth_sensor() + self.g_depth_scale = depth_sensor.get_depth_scale() + + self.intrinsics = profile.get_stream(rs.stream.color).as_video_stream_profile().get_intrinsics() + + def get_frame(self): + frames = self.pipeline.wait_for_frames() + aligned_frames = self.align.process(frames) + color_frame = aligned_frames.get_color_frame() + + if self.enable_depth: + depth_frame = aligned_frames.get_depth_frame() + + if not color_frame: + return None + + color_image = np.asanyarray(color_frame.get_data()) + # color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB) + depth_image = np.asanyarray(depth_frame.get_data()) if self.enable_depth else None + return color_image, depth_image + + def release(self): + self.pipeline.stop() + + +class OpenCVCamera: + def __init__(self, device_id, img_shape, fps): + """ + decive_id: /dev/video* or * + img_shape: [height, width] + """ + self.id = device_id + self.fps = fps + self.img_shape = img_shape + self.cap = cv2.VideoCapture(self.id, cv2.CAP_V4L2) + self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc("M", "J", "P", "G")) + self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.img_shape[0]) + self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.img_shape[1]) + self.cap.set(cv2.CAP_PROP_FPS, self.fps) + + # Test if the camera can read frames + if not self._can_read_frame(): + logger_mp.error( + f"[Image Server] Camera {self.id} Error: Failed to initialize the camera or read frames. Exiting..." + ) + self.release() + + def _can_read_frame(self): + success, _ = self.cap.read() + return success + + def release(self): + self.cap.release() + + def get_frame(self): + ret, color_image = self.cap.read() + if not ret: + return None + return color_image + + +class ImageServer: + def __init__(self, config, port=5554, Unit_Test=False): + """ + config example1: + { + 'fps':30 # frame per second + 'head_camera_type': 'opencv', # opencv or realsense + 'head_camera_image_shape': [480, 1280], # Head camera resolution [height, width] + 'head_camera_id_numbers': [0], # '/dev/video0' (opencv) + 'wrist_camera_type': 'realsense', + 'wrist_camera_image_shape': [480, 640], # Wrist camera resolution [height, width] + 'wrist_camera_id_numbers': ["218622271789", "241222076627"], # realsense camera's serial number + } + + config example2: + { + 'fps':30 # frame per second + 'head_camera_type': 'realsense', # opencv or realsense + 'head_camera_image_shape': [480, 640], # Head camera resolution [height, width] + 'head_camera_id_numbers': ["218622271739"], # realsense camera's serial number + 'wrist_camera_type': 'opencv', + 'wrist_camera_image_shape': [480, 640], # Wrist camera resolution [height, width] + 'wrist_camera_id_numbers': [0,1], # '/dev/video0' and '/dev/video1' (opencv) + } + + If you are not using the wrist camera, you can comment out its configuration, like this below: + config: + { + 'fps':30 # frame per second + 'head_camera_type': 'opencv', # opencv or realsense + 'head_camera_image_shape': [480, 1280], # Head camera resolution [height, width] + 'head_camera_id_numbers': [0], # '/dev/video0' (opencv) + #'wrist_camera_type': 'realsense', + #'wrist_camera_image_shape': [480, 640], # Wrist camera resolution [height, width] + #'wrist_camera_id_numbers': ["218622271789", "241222076627"], # serial number (realsense) + } + """ + logger_mp.info(config) + self.fps = config.get("fps", 30) + self.head_camera_type = config.get("head_camera_type", "opencv") + self.head_image_shape = config.get("head_camera_image_shape", [480, 640]) # (height, width) + self.head_camera_id_numbers = config.get("head_camera_id_numbers", [0]) + + self.wrist_camera_type = config.get("wrist_camera_type", None) + self.wrist_image_shape = config.get("wrist_camera_image_shape", [480, 640]) # (height, width) + self.wrist_camera_id_numbers = config.get("wrist_camera_id_numbers", None) + + self.port = port + self.Unit_Test = Unit_Test + + # Initialize head cameras + self.head_cameras = [] + if self.head_camera_type == "opencv": + for device_id in self.head_camera_id_numbers: + camera = OpenCVCamera(device_id=device_id, img_shape=self.head_image_shape, fps=self.fps) + self.head_cameras.append(camera) + elif self.head_camera_type == "realsense": + for serial_number in self.head_camera_id_numbers: + camera = RealSenseCamera(img_shape=self.head_image_shape, fps=self.fps, serial_number=serial_number) + self.head_cameras.append(camera) + else: + logger_mp.warning(f"[Image Server] Unsupported head_camera_type: {self.head_camera_type}") + + # Initialize wrist cameras if provided + self.wrist_cameras = [] + if self.wrist_camera_type and self.wrist_camera_id_numbers: + if self.wrist_camera_type == "opencv": + for device_id in self.wrist_camera_id_numbers: + camera = OpenCVCamera(device_id=device_id, img_shape=self.wrist_image_shape, fps=self.fps) + self.wrist_cameras.append(camera) + elif self.wrist_camera_type == "realsense": + for serial_number in self.wrist_camera_id_numbers: + camera = RealSenseCamera( + img_shape=self.wrist_image_shape, fps=self.fps, serial_number=serial_number + ) + self.wrist_cameras.append(camera) + else: + logger_mp.warning(f"[Image Server] Unsupported wrist_camera_type: {self.wrist_camera_type}") + + # Set ZeroMQ context and socket + self.context = zmq.Context() + self.socket = self.context.socket(zmq.PUB) + self.socket.bind(f"tcp://*:{self.port}") + + if self.Unit_Test: + self._init_performance_metrics() + + for cam in self.head_cameras: + if isinstance(cam, OpenCVCamera): + logger_mp.info( + f"[Image Server] Head camera {cam.id} resolution: {cam.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)} x {cam.cap.get(cv2.CAP_PROP_FRAME_WIDTH)}" + ) + elif isinstance(cam, RealSenseCamera): + logger_mp.info( + f"[Image Server] Head camera {cam.serial_number} resolution: {cam.img_shape[0]} x {cam.img_shape[1]}" + ) + else: + logger_mp.warning("[Image Server] Unknown camera type in head_cameras.") + + for cam in self.wrist_cameras: + if isinstance(cam, OpenCVCamera): + logger_mp.info( + f"[Image Server] Wrist camera {cam.id} resolution: {cam.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)} x {cam.cap.get(cv2.CAP_PROP_FRAME_WIDTH)}" + ) + elif isinstance(cam, RealSenseCamera): + logger_mp.info( + f"[Image Server] Wrist camera {cam.serial_number} resolution: {cam.img_shape[0]} x {cam.img_shape[1]}" + ) + else: + logger_mp.warning("[Image Server] Unknown camera type in wrist_cameras.") + + logger_mp.info("[Image Server] Image server has started, waiting for client connections...") + + def _init_performance_metrics(self): + self.frame_count = 0 # Total frames sent + self.time_window = 1.0 # Time window for FPS calculation (in seconds) + self.frame_times = deque() # Timestamps of frames sent within the time window + self.start_time = time.time() # Start time of the streaming + + def _update_performance_metrics(self, current_time): + # Add current time to frame times deque + self.frame_times.append(current_time) + # Remove timestamps outside the time window + while self.frame_times and self.frame_times[0] < current_time - self.time_window: + self.frame_times.popleft() + # Increment frame count + self.frame_count += 1 + + def _print_performance_metrics(self, current_time): + if self.frame_count % 30 == 0: + elapsed_time = current_time - self.start_time + real_time_fps = len(self.frame_times) / self.time_window + logger_mp.info( + f"[Image Server] Real-time FPS: {real_time_fps:.2f}, Total frames sent: {self.frame_count}, Elapsed time: {elapsed_time:.2f} sec" + ) + + def _close(self): + for cam in self.head_cameras: + cam.release() + for cam in self.wrist_cameras: + cam.release() + self.socket.close() + self.context.term() + logger_mp.info("[Image Server] The server has been closed.") + + def send_process(self): + try: + while True: + head_frames = [] + for cam in self.head_cameras: + if self.head_camera_type == "opencv": + color_image = cam.get_frame() + if color_image is None: + logger_mp.error("[Image Server] Head camera frame read is error.") + break + elif self.head_camera_type == "realsense": + color_image, depth_iamge = cam.get_frame() + if color_image is None: + logger_mp.error("[Image Server] Head camera frame read is error.") + break + head_frames.append(color_image) + if len(head_frames) != len(self.head_cameras): + break + head_color = cv2.hconcat(head_frames) + + if self.wrist_cameras: + wrist_frames = [] + for cam in self.wrist_cameras: + if self.wrist_camera_type == "opencv": + color_image = cam.get_frame() + if color_image is None: + logger_mp.error("[Image Server] Wrist camera frame read is error.") + break + elif self.wrist_camera_type == "realsense": + color_image, depth_iamge = cam.get_frame() + if color_image is None: + logger_mp.error("[Image Server] Wrist camera frame read is error.") + break + wrist_frames.append(color_image) + wrist_color = cv2.hconcat(wrist_frames) + + # Concatenate head and wrist frames + full_color = cv2.hconcat([head_color, wrist_color]) + else: + full_color = head_color + + ret, buffer = cv2.imencode(".jpg", full_color) + if not ret: + logger_mp.error("[Image Server] Frame imencode is failed.") + continue + + jpg_bytes = buffer.tobytes() + + if self.Unit_Test: + timestamp = time.time() + frame_id = self.frame_count + header = struct.pack("dI", timestamp, frame_id) # 8-byte double, 4-byte unsigned int + message = header + jpg_bytes + else: + message = jpg_bytes + + self.socket.send(message) + + if self.Unit_Test: + current_time = time.time() + self._update_performance_metrics(current_time) + self._print_performance_metrics(current_time) + + except KeyboardInterrupt: + logger_mp.warning("[Image Server] Interrupted by user.") + finally: + self._close() + + +if __name__ == "__main__": + # config = { + # "fps": 30, + # "head_camera_type": "opencv", + # "head_camera_image_shape": [480, 1280], # Head camera resolution + # "head_camera_id_numbers": [0], + # "wrist_camera_type": "opencv", + # "wrist_camera_image_shape": [480, 640], # Wrist camera resolution + # "wrist_camera_id_numbers": [2, 4], + # + #infrared + # config = { + # "fps": 30, + # "head_camera_type": "opencv", + # "head_camera_image_shape": [480, 640], + # "head_camera_id_numbers": [2], # <-- wrist cam that reported 480x640 + # # no wrist_* keys + # } + #rgb + config = { + "fps": 30, + "head_camera_type": "opencv", + "head_camera_image_shape": [480,1280], # match the device + "head_camera_id_numbers": [4], # /dev/video4 is RGB +} + + server = ImageServer(config, Unit_Test=False) + server.send_process() diff --git a/eval_robot/make_robot.py b/eval_robot/make_robot.py new file mode 100644 index 000000000..9fe0f7622 --- /dev/null +++ b/eval_robot/make_robot.py @@ -0,0 +1,298 @@ +from multiprocessing import shared_memory, Value, Array, Lock +from typing import Any +import numpy as np +import argparse +import threading +import torch +from unitree_lerobot.eval_robot.image_server.image_client import ImageClient +from unitree_lerobot.eval_robot.robot_control.robot_arm import ( + G1_29_ArmController, + G1_23_ArmController, +) +from unitree_lerobot.eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK, G1_23_ArmIK +from unitree_lerobot.eval_robot.robot_control.robot_hand_unitree import ( + Dex3_1_Controller, + Dex1_1_Gripper_Controller, +) + +from unitree_lerobot.eval_robot.utils.episode_writer import EpisodeWriter + +from unitree_lerobot.eval_robot.robot_control.robot_hand_inspire import Inspire_Controller +from unitree_lerobot.eval_robot.robot_control.robot_hand_brainco import Brainco_Controller + + +from unitree_sdk2py.core.channel import ChannelPublisher +from unitree_sdk2py.idl.std_msgs.msg.dds_ import String_ + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + +# Configuration for robot arms +ARM_CONFIG = { + "G1_29": {"controller": G1_29_ArmController, "ik_solver": G1_29_ArmIK, "dof": 14}, + "G1_23": {"controller": G1_23_ArmController, "ik_solver": G1_23_ArmIK, "dof": 14}, + # Add other arms here +} + +# Configuration for end-effectors +EE_CONFIG: dict[str, dict[str, Any]] = { + "dex3": { + "controller": Dex3_1_Controller, + "dof": 7, + "shared_mem_type": "Array", + "shared_mem_size": 7, + # "out_len": 14, + }, + "dex1": { + "controller": Dex1_1_Gripper_Controller, + "dof": 1, + "shared_mem_type": "Value", + # "out_len": 2, + }, + "inspire1": { + "controller": Inspire_Controller, + "dof": 6, + "shared_mem_type": "Array", + "shared_mem_size": 6, + # "out_len": 12, + }, + "brainco": { + "controller": Brainco_Controller, + "dof": 6, + "shared_mem_type": "Array", + "shared_mem_size": 6, + # "out_len": 12, + }, +} + + +def setup_image_client(args: argparse.Namespace) -> dict[str, Any]: + """Initializes and starts the image client and shared memory.""" + # image client: img_config should be the same as the configuration in image_server.py (of Robot's development computing unit) + if getattr(args, "sim", False): + img_config = { + "fps": 30, + "head_camera_type": "opencv", + "head_camera_image_shape": [480, 640], # Head camera resolution + "head_camera_id_numbers": [0], + "wrist_camera_type": "opencv", + "wrist_camera_image_shape": [480, 640], # Wrist camera resolution + "wrist_camera_id_numbers": [2, 4], + } + else: + img_config = {#we dont have wrist for now + "fps": 30, + "head_camera_type": "opencv", + "head_camera_image_shape": [480, 640], # Head camera resolution + "head_camera_id_numbers": [0], + #"wrist_camera_type": "opencv", + #"wrist_camera_image_shape": [480, 640], # Wrist camera resolution + #"wrist_camera_id_numbers": [2, 4], + } + + ASPECT_RATIO_THRESHOLD = 2.0 # If the aspect ratio exceeds this value, it is considered binocular + if len(img_config["head_camera_id_numbers"]) > 1 or ( + img_config["head_camera_image_shape"][1] / img_config["head_camera_image_shape"][0] > ASPECT_RATIO_THRESHOLD + ): + BINOCULAR = True + else: + BINOCULAR = False + if "wrist_camera_type" in img_config: + WRIST = True + else: + WRIST = False + # BINOCULAR = True#add flag properly + if BINOCULAR and not ( + img_config["head_camera_image_shape"][1] / img_config["head_camera_image_shape"][0] > ASPECT_RATIO_THRESHOLD + ): + tv_img_shape = (img_config["head_camera_image_shape"][0], img_config["head_camera_image_shape"][1] * 2, 3) + else: + tv_img_shape = (img_config["head_camera_image_shape"][0], img_config["head_camera_image_shape"][1], 3) + + tv_img_shm = shared_memory.SharedMemory(create=True, size=np.prod(tv_img_shape) * np.uint8().itemsize) + tv_img_array = np.ndarray(tv_img_shape, dtype=np.uint8, buffer=tv_img_shm.buf) + + # Initialize wrist camera variables + wrist_img_shm = None + wrist_img_array = None + wrist_img_shape = None + + if WRIST and getattr(args, "sim", False): + wrist_img_shape = (img_config["wrist_camera_image_shape"][0], img_config["wrist_camera_image_shape"][1] * 2, 3) + wrist_img_shm = shared_memory.SharedMemory(create=True, size=np.prod(wrist_img_shape) * np.uint8().itemsize) + wrist_img_array = np.ndarray(wrist_img_shape, dtype=np.uint8, buffer=wrist_img_shm.buf) + img_client = ImageClient( + tv_img_shape=tv_img_shape, + tv_img_shm_name=tv_img_shm.name, + wrist_img_shape=wrist_img_shape, + wrist_img_shm_name=wrist_img_shm.name, + server_address="127.0.0.1", + ) + elif WRIST and not getattr(args, "sim", False): + wrist_img_shape = (img_config["wrist_camera_image_shape"][0], img_config["wrist_camera_image_shape"][1] * 2, 3) + wrist_img_shm = shared_memory.SharedMemory(create=True, size=np.prod(wrist_img_shape) * np.uint8().itemsize) + wrist_img_array = np.ndarray(wrist_img_shape, dtype=np.uint8, buffer=wrist_img_shm.buf) + img_client = ImageClient( + tv_img_shape=tv_img_shape, + tv_img_shm_name=tv_img_shm.name, + wrist_img_shape=wrist_img_shape, + wrist_img_shm_name=wrist_img_shm.name, + ) + else: + img_client = ImageClient(tv_img_shape=tv_img_shape, tv_img_shm_name=tv_img_shm.name) + + has_wrist_cam = "wrist_camera_type" in img_config + + image_receive_thread = threading.Thread(target=img_client.receive_process, daemon=True) + image_receive_thread.daemon = True + image_receive_thread.start() + + # Only include shared memory objects that exist + shm_resources = [tv_img_shm] + if wrist_img_shm is not None: + shm_resources.append(wrist_img_shm) + + return { + "tv_img_array": tv_img_array, + "wrist_img_array": wrist_img_array, + "tv_img_shape": tv_img_shape, + "wrist_img_shape": wrist_img_shape, + "is_binocular": BINOCULAR, + "has_wrist_cam": has_wrist_cam, + "shm_resources": shm_resources, + } + + +def _resolve_out_len(spec: dict[str, Any]) -> int: + return int(spec.get("out_len", 2 * int(spec["dof"]))) + + +def setup_robot_interface(args: argparse.Namespace) -> dict[str, Any]: + """ + Initializes robot controllers and IK solvers based on configuration. + """ + # ---------- Arm ---------- + arm_spec = ARM_CONFIG[args.arm] + arm_ik = arm_spec["ik_solver"]() + is_sim = getattr(args, "sim", False) + motion_mode = getattr(args, "motion", False) + arm_ctrl = arm_spec["controller"](motion_mode=motion_mode, simulation_mode=is_sim) + + result = { + "arm_ctrl": arm_ctrl, + "arm_ik": arm_ik, + "arm_dof": int(arm_spec["dof"]), + } + + # ---------- Simulation Components ---------- + if is_sim: + + from unitree_lerobot.eval_robot.utils.sim_state_topic import start_sim_state_subscribe, start_sim_reward_subscribe + from unitree_lerobot.eval_robot.utils.episode_writer import EpisodeWriter + from unitree_sdk2py.core.channel import ChannelPublisher, ChannelFactoryInitialize + from unitree_sdk2py.idl.std_msgs.msg.dds_ import String_ + + # Create simulation DDS subscribers + sim_state_subscriber = start_sim_state_subscribe() + sim_reward_subscriber = start_sim_reward_subscribe() + + # Initialize DDS channel 1 for simulation mode (if not already initialized) + try: + ChannelFactoryInitialize(1) + except: + pass # May already be initialized by arm controller + + # Create reset pose publisher (publishes to rt/reset_pose/cmd to match simulation) + reset_pose_publisher = ChannelPublisher("rt/reset_pose/cmd", String_) + reset_pose_publisher.Init() + + # Create episode writer for data collection + episode_writer = EpisodeWriter( + task_dir=args.task_dir if hasattr(args, 'task_dir') else "./data", + frequency=args.frequency if hasattr(args, 'frequency') else 30 + ) + + result.update({ + "sim_state_subscriber": sim_state_subscriber, + "sim_reward_subscriber": sim_reward_subscriber, + "reset_pose_publisher": reset_pose_publisher, + "episode_writer": episode_writer, + }) + + # ---------- End Effector ---------- + if hasattr(args, 'ee') and args.ee: + ee_spec = EE_CONFIG.get(args.ee) + if ee_spec: + result["ee_dof"] = int(ee_spec["dof"]) + # Add end effector shared memory setup here if needed + result["ee_shared_mem"] = {"lock": None, "state": [], "left": [], "right": []} + else: + result["ee_dof"] = 0 + result["ee_shared_mem"] = {"lock": None, "state": [], "left": [], "right": []} + else: + result["ee_dof"] = 0 + result["ee_shared_mem"] = {"lock": None, "state": [], "left": [], "right": []} + + EE_DOF = 7 + left_in = Array("d", EE_DOF, lock=True) # desired left finger q targets + right_in = Array("d", EE_DOF, lock=True) # desired right finger q targets + + # outputs (states + echoed actions), both length 14 (left 7 + right 7) + dual_state_out = Array("d", 2*EE_DOF, lock=True) + dual_action_out = Array("d", 2*EE_DOF, lock=True) + dual_lock = Lock() + + if getattr(args, 'ee', None) == 'dex3': + from robot_control.robot_hand_unitree import Dex3_1_Controller + ctrl = Dex3_1_Controller( + left_hand_array_in=left_in, + right_hand_array_in=right_in, + dual_hand_data_lock=dual_lock, + dual_hand_state_array_out=dual_state_out, + dual_hand_action_array_out=dual_action_out, + fps=100.0, + Unit_Test=False, + simulation_mode=getattr(args, "sim", False), # True in sim + ) + result["ee_dof"] = EE_DOF + result["ee_shared_mem"] = { + "lock": dual_lock, + "state": dual_state_out, # 14-length: [L7, R7] + "left": left_in, # input commands for left hand + "right": right_in, # input commands for right hand + } + + return result + + +def process_images_and_observations( + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam, arm_ctrl +): + """Processes images and generates observations.""" + current_tv_image = tv_img_array.copy() + current_wrist_image = wrist_img_array.copy() if has_wrist_cam else None + + left_top_cam = current_tv_image[:, : tv_img_shape[1] // 2] if is_binocular else current_tv_image + right_top_cam = current_tv_image[:, tv_img_shape[1] // 2 :] if is_binocular else left_top_cam.copy() + + left_wrist_cam = right_wrist_cam = None + if has_wrist_cam and current_wrist_image is not None: + left_wrist_cam = current_wrist_image[:, : wrist_img_shape[1] // 2] + right_wrist_cam = current_wrist_image[:, wrist_img_shape[1] // 2 :] + observation = { + "observation.image": torch.from_numpy(left_top_cam), + "observation.images.cam_right_high": torch.from_numpy(right_top_cam), + "observation.images.cam_left_wrist": torch.from_numpy(left_wrist_cam) if has_wrist_cam else np.zeros((480, 640, 3)), + "observation.images.cam_right_wrist": torch.from_numpy(right_wrist_cam) if has_wrist_cam else np.zeros((480, 640, 3)), + } + current_arm_q = arm_ctrl.get_current_dual_arm_q() + + return observation, current_arm_q + + +def publish_reset_category(category: int, publisher): # Scene Reset signal + msg = String_(data=str(category)) + publisher.Write(msg) + logger_mp.info(f"published reset category: {category}") diff --git a/eval_robot/replay_robot.py b/eval_robot/replay_robot.py new file mode 100644 index 000000000..1edf47d75 --- /dev/null +++ b/eval_robot/replay_robot.py @@ -0,0 +1,118 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import numpy as np + +from multiprocessing.sharedctypes import SynchronizedArray +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from unitree_lerobot.eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from unitree_lerobot.eval_robot.utils.utils import cleanup_resources, EvalRealConfig + +from unitree_lerobot.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data +from unitree_lerobot.eval_robot.utils.utils import to_list, to_scalar + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +@parser.wrap() +def replay_main(cfg: EvalRealConfig): + logger_mp.info(f"Arguments: {cfg}") + + if cfg.visualization: + rerun_logger = RerunLogger() + + image_info = setup_image_client(cfg) + robot_interface = setup_robot_interface(cfg) + + """The main control and evaluation loop.""" + # Unpack interfaces for convenience + arm_ctrl, arm_ik, ee_shared_mem, arm_dof, ee_dof = ( + robot_interface[key] for key in ["arm_ctrl", "arm_ik", "ee_shared_mem", "arm_dof", "ee_dof"] + ) + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam = ( + image_info[key] + for key in [ + "tv_img_array", + "wrist_img_array", + "tv_img_shape", + "wrist_img_shape", + "is_binocular", + "has_wrist_cam", + ] + ) + + logger_mp.info(f"Starting evaluation loop at {cfg.frequency} Hz.") + + dataset = LeRobotDataset(repo_id=cfg.repo_id, root=cfg.root, episodes=[cfg.episodes]) + actions = dataset.hf_dataset.select_columns("action") + + # init pose + from_idx = dataset.episode_data_index["from"][0].item() + step = dataset[from_idx] + init_left_arm_pose = step["observation.state"][:14].cpu().numpy() + + user_input = input("Please enter the start signal (enter 's' to start the subsequent program):") + if user_input.lower() == "s": + # "The initial positions of the robot's arm and fingers take the initial positions during data recording." + logger_mp.info("Initializing robot to starting pose...") + tau = arm_ik.solve_tau(init_left_arm_pose) + arm_ctrl.ctrl_dual_arm(init_left_arm_pose, tau) + time.sleep(1) + for idx in range(dataset.num_frames): + loop_start_time = time.perf_counter() + + left_ee_state = right_ee_state = np.array([]) + action_np = actions[idx]["action"].numpy() + + # exec action + arm_action = action_np[:arm_dof] + tau = arm_ik.solve_tau(arm_action) + arm_ctrl.ctrl_dual_arm(arm_action, tau) + logger_mp.info(f"arm_action {arm_action}, tau {tau}") + + if cfg.ee: + ee_action_start_idx = arm_dof + left_ee_action = action_np[ee_action_start_idx : ee_action_start_idx + ee_dof] + right_ee_action = action_np[ee_action_start_idx + ee_dof : ee_action_start_idx + 2 * ee_dof] + logger_mp.info(f"EE Action: left {left_ee_action}, right {right_ee_action}") + + with ee_shared_mem["lock"]: + full_state = np.array(ee_shared_mem["state"][:]) + left_ee_state = full_state[:ee_dof] + right_ee_state = full_state[ee_dof:] + + if isinstance(ee_shared_mem["left"], SynchronizedArray): + ee_shared_mem["left"][:] = to_list(left_ee_action) + ee_shared_mem["right"][:] = to_list(right_ee_action) + elif hasattr(ee_shared_mem["left"], "value") and hasattr(ee_shared_mem["right"], "value"): + ee_shared_mem["left"].value = to_scalar(left_ee_action) + ee_shared_mem["right"].value = to_scalar(right_ee_action) + + if cfg.visualization: + observation, current_arm_q = process_images_and_observations( + tv_img_array, wrist_img_array, tv_img_shape, wrist_img_shape, is_binocular, has_wrist_cam, arm_ctrl + ) + state = np.concatenate((current_arm_q, left_ee_state, right_ee_state)) + + visualization_data(idx, observation, state, action_np, rerun_logger) + + # Maintain frequency + time.sleep(max(0, (1.0 / cfg.frequency) - (time.perf_counter() - loop_start_time))) + + cleanup_resources(image_info) + + +if __name__ == "__main__": + replay_main() diff --git a/eval_robot/replay_working.py b/eval_robot/replay_working.py new file mode 100644 index 000000000..06addefd5 --- /dev/null +++ b/eval_robot/replay_working.py @@ -0,0 +1,88 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import numpy as np +import cv2 +from multiprocessing.sharedctypes import SynchronizedArray +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.robots.unitree_g1.eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from lerobot.robots.unitree_g1.eval_robot.utils.utils import cleanup_resources, EvalRealConfig + +from lerobot.robots.unitree_g1.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data +from lerobot.robots.unitree_g1.eval_robot.utils.utils import to_list, to_scalar +from lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_test import ( + G1_29_ArmController, G1_29_JointIndex +) +import logging_mp +from lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def replay_main(): + + #damp needs to be on? do i start the robot as well + + arm_ik = G1_29_ArmIK() + arm_ctrl = G1_29_ArmController(motion_mode=False, simulation_mode=False)#motors move here upon init. there's a bug where when closing python the motors die + #arm_ctrl.ctrl_dual_arm_go_home() + dataset = LeRobotDataset(repo_id='nepyope/unitree_box_push_30fps_actions_fix', root="", episodes=[0]) + actions = dataset.hf_dataset.select_columns("action") + print(actions) + episode_idx = 8 + episode_info = dataset.meta.episodes[episode_idx] + + from_idx = episode_info["dataset_from_index"] + to_idx = episode_info["dataset_to_index"] + step = dataset[from_idx] + init_left_arm_pose = step["observation.state"][:14].cpu().numpy() + tau = arm_ik.solve_tau(init_left_arm_pose) + #arm_ctrl.ctrl_dual_arm(init_left_arm_pose, tau) + print('ok') + + # Create config object for image client + import argparse + cfg = argparse.Namespace( + sim=False, # Real robot (not simulation) + arm="G1_29", + ee="dex3", + motion=False + ) + + #replay actions from the dataset + for idx in range(dataset.num_frames): + + arm_joint_indices = set(range(15, 29)) # 15–28 are arms + for jid in G1_29_JointIndex: + if jid.value not in arm_joint_indices: + arm_ctrl.msg.motor_cmd[jid].mode = 1 + arm_ctrl.msg.motor_cmd[jid].q = 0.0 + arm_ctrl.msg.motor_cmd[jid].dq = 0.0 + arm_ctrl.msg.motor_cmd[jid].tau = 0.0 + loop_start_time = time.perf_counter() + + action_np = actions[idx]["action"].numpy() + + # exec action + arm_action = action_np[:14] + tau = arm_ik.solve_tau(arm_action) + arm_ctrl.ctrl_dual_arm(arm_action, tau) + logger_mp.info(f"arm_action {arm_action}, tau {tau}") + + # Maintain frequency + time.sleep(max(0, (1.0 / 30) - (time.perf_counter() - loop_start_time))) + + #some thread issue idk motion_mode true gets rid of the shakes motion mode + +if __name__ == "__main__": + replay_main() diff --git a/eval_robot/robot_control/robot_arm.py b/eval_robot/robot_control/robot_arm.py new file mode 100644 index 000000000..63e096cb3 --- /dev/null +++ b/eval_robot/robot_control/robot_arm.py @@ -0,0 +1,1207 @@ +import numpy as np +import threading +import time +from enum import IntEnum + +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as hg_LowCmd, LowState_ as hg_LowState # idl for g1, h1_2 +from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_ +from unitree_sdk2py.utils.crc import CRC + +from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowCmd_ as go_LowCmd, LowState_ as go_LowState # idl for h1 +from unitree_sdk2py.idl.default import unitree_go_msg_dds__LowCmd_ + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + +kTopicLowCommand_Debug = "rt/lowcmd" +kTopicLowCommand_Motion = "rt/arm_sdk" +kTopicLowState = "rt/lowstate" + +G1_29_Num_Motors = 35 +G1_23_Num_Motors = 35 +H1_2_Num_Motors = 35 +H1_Num_Motors = 20 + + +class MotorState: + def __init__(self): + self.q = None + self.dq = None + + +class G1_29_LowState: + def __init__(self): + self.motor_state = [MotorState() for _ in range(G1_29_Num_Motors)] + + +class G1_23_LowState: + def __init__(self): + self.motor_state = [MotorState() for _ in range(G1_23_Num_Motors)] + + +class H1_2_LowState: + def __init__(self): + self.motor_state = [MotorState() for _ in range(H1_2_Num_Motors)] + + +class H1_LowState: + def __init__(self): + self.motor_state = [MotorState() for _ in range(H1_Num_Motors)] + + +class DataBuffer: + def __init__(self): + self.data = None + self.lock = threading.Lock() + + def GetData(self): + with self.lock: + return self.data + + def SetData(self, data): + with self.lock: + self.data = data + + +class G1_29_ArmController: + def __init__(self, motion_mode=False, simulation_mode=False): + logger_mp.info("Initialize G1_29_ArmController...") + self.q_target = np.zeros(14) + self.tauff_target = np.zeros(14) + self.motion_mode = motion_mode + self.simulation_mode = simulation_mode + self.kp_high = 300.0 + self.kd_high = 3.0 + self.kp_low = 80.0 + self.kd_low = 3.0 + self.kp_wrist = 40.0 + self.kd_wrist = 1.5 + + self.all_motor_q = None + self.arm_velocity_limit = 20.0 + self.control_dt = 1.0 / 250.0 + + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None + + # initialize lowcmd publisher and lowstate subscriber + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + if self.motion_mode: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Motion, hg_LowCmd) + else: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd) + self.lowcmd_publisher.Init() + self.lowstate_subscriber = ChannelSubscriber(kTopicLowState, hg_LowState) + self.lowstate_subscriber.Init() + self.lowstate_buffer = DataBuffer() + + # initialize subscribe thread + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() + + while not self.lowstate_buffer.GetData(): + time.sleep(0.1) + logger_mp.warning("[G1_29_ArmController] Waiting to subscribe dds...") + logger_mp.info("[G1_29_ArmController] Subscribe dds ok.") + + # initialize hg's lowcmd msg + self.crc = CRC() + self.msg = unitree_hg_msg_dds__LowCmd_() + self.msg.mode_pr = 0 + self.msg.mode_machine = self.get_mode_machine() + + self.all_motor_q = self.get_current_motor_q() + logger_mp.info(f"Current all body motor state q:\n{self.all_motor_q} \n") + logger_mp.info(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + logger_mp.info("Lock all joints except two arms...\n") + + arm_indices = set(member.value for member in G1_29_JointArmIndex) + for id in G1_29_JointIndex: + self.msg.motor_cmd[id].mode = 1 + if id.value in arm_indices: + if self._Is_wrist_motor(id): + self.msg.motor_cmd[id].kp = self.kp_wrist + self.msg.motor_cmd[id].kd = self.kd_wrist + else: + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].q = self.all_motor_q[id] + logger_mp.info("Lock OK!\n") + + # initialize publish thread + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() + + logger_mp.info("Initialize G1_29_ArmController OK!\n") + + def _subscribe_motor_state(self): + while True: + msg = self.lowstate_subscriber.Read() + if msg is not None: + lowstate = G1_29_LowState() + for id in range(G1_29_Num_Motors): + lowstate.motor_state[id].q = msg.motor_state[id].q + lowstate.motor_state[id].dq = msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) + time.sleep(0.002) + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): + if self.motion_mode: + self.msg.motor_cmd[G1_29_JointIndex.kNotUsedJoint0].q = 1.0 + + while True: + start_time = time.time() + + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + if self.simulation_mode: + cliped_arm_q_target = arm_q_target + else: + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit=self.arm_velocity_limit) + + for idx, id in enumerate(G1_29_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # logger_mp.debug(f"arm_velocity_limit:{self.arm_velocity_limit}") + # logger_mp.debug(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + """Set control target values q & tau of the left and right arm motors.""" + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target + + def get_mode_machine(self): + """Return current dds mode machine.""" + return self.lowstate_subscriber.Read().mode_machine + + def get_current_motor_q(self): + """Return current state q of all body motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointIndex]) + + def get_current_dual_arm_q(self): + """Return current state q of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointArmIndex]) + + def get_current_dual_arm_dq(self): + """Return current state dq of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in G1_29_JointArmIndex]) + + def ctrl_dual_arm_go_home(self): + """Move both the left and right arms of the robot to their home position by setting the target joint angles (q) and torques (tau) to zero.""" + logger_mp.info("[G1_29_ArmController] ctrl_dual_arm_go_home start...") + max_attempts = 100 + current_attempts = 0 + with self.ctrl_lock: + self.q_target = np.zeros(14) + # self.tauff_target = np.zeros(14) + tolerance = 0.05 # Tolerance threshold for joint angles to determine "close to zero", can be adjusted based on your motor's precision requirements + while current_attempts < max_attempts: + current_q = self.get_current_dual_arm_q() + if np.all(np.abs(current_q) < tolerance): + if self.motion_mode: + for weight in np.linspace(1, 0, num=101): + self.msg.motor_cmd[G1_29_JointIndex.kNotUsedJoint0].q = weight + time.sleep(0.02) + logger_mp.info("[G1_29_ArmController] both arms have reached the home position.") + break + current_attempts += 1 + time.sleep(0.05) + + def speed_gradual_max(self, t=5.0): + """Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.""" + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True + + def speed_instant_max(self): + """set arms velocity to the maximum value immediately, instead of gradually increasing.""" + self.arm_velocity_limit = 30.0 + + def _Is_weak_motor(self, motor_index): + weak_motors = [ + G1_29_JointIndex.kLeftAnklePitch.value, + G1_29_JointIndex.kRightAnklePitch.value, + # Left arm + G1_29_JointIndex.kLeftShoulderPitch.value, + G1_29_JointIndex.kLeftShoulderRoll.value, + G1_29_JointIndex.kLeftShoulderYaw.value, + G1_29_JointIndex.kLeftElbow.value, + # Right arm + G1_29_JointIndex.kRightShoulderPitch.value, + G1_29_JointIndex.kRightShoulderRoll.value, + G1_29_JointIndex.kRightShoulderYaw.value, + G1_29_JointIndex.kRightElbow.value, + ] + return motor_index.value in weak_motors + + def _Is_wrist_motor(self, motor_index): + wrist_motors = [ + G1_29_JointIndex.kLeftWristRoll.value, + G1_29_JointIndex.kLeftWristPitch.value, + G1_29_JointIndex.kLeftWristyaw.value, + G1_29_JointIndex.kRightWristRoll.value, + G1_29_JointIndex.kRightWristPitch.value, + G1_29_JointIndex.kRightWristYaw.value, + ] + return motor_index.value in wrist_motors + + +class G1_29_JointArmIndex(IntEnum): + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + + +class G1_29_JointIndex(IntEnum): + # Left leg + kLeftHipPitch = 0 + kLeftHipRoll = 1 + kLeftHipYaw = 2 + kLeftKnee = 3 + kLeftAnklePitch = 4 + kLeftAnkleRoll = 5 + + # Right leg + kRightHipPitch = 6 + kRightHipRoll = 7 + kRightHipYaw = 8 + kRightKnee = 9 + kRightAnklePitch = 10 + kRightAnkleRoll = 11 + + kWaistYaw = 12 + kWaistRoll = 13 + kWaistPitch = 14 + + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + + # not used + kNotUsedJoint0 = 29 + kNotUsedJoint1 = 30 + kNotUsedJoint2 = 31 + kNotUsedJoint3 = 32 + kNotUsedJoint4 = 33 + kNotUsedJoint5 = 34 + + +class G1_23_ArmController: + def __init__(self, motion_mode=False, simulation_mode=False): + self.simulation_mode = simulation_mode + self.motion_mode = motion_mode + + logger_mp.info("Initialize G1_23_ArmController...") + self.q_target = np.zeros(10) + self.tauff_target = np.zeros(10) + + self.kp_high = 300.0 + self.kd_high = 3.0 + self.kp_low = 80.0 + self.kd_low = 3.0 + self.kp_wrist = 40.0 + self.kd_wrist = 1.5 + + self.all_motor_q = None + self.arm_velocity_limit = 20.0 + self.control_dt = 1.0 / 250.0 + + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None + + # initialize lowcmd publisher and lowstate subscriber + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + if self.motion_mode: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Motion, hg_LowCmd) + else: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd) + self.lowcmd_publisher.Init() + self.lowstate_subscriber = ChannelSubscriber(kTopicLowState, hg_LowState) + self.lowstate_subscriber.Init() + self.lowstate_buffer = DataBuffer() + + # initialize subscribe thread + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() + + while not self.lowstate_buffer.GetData(): + time.sleep(0.1) + logger_mp.warning("[G1_23_ArmController] Waiting to subscribe dds...") + logger_mp.info("[G1_23_ArmController] Subscribe dds ok.") + + # initialize hg's lowcmd msg + self.crc = CRC() + self.msg = unitree_hg_msg_dds__LowCmd_() + self.msg.mode_pr = 0 + self.msg.mode_machine = self.get_mode_machine() + + self.all_motor_q = self.get_current_motor_q() + logger_mp.info(f"Current all body motor state q:\n{self.all_motor_q} \n") + logger_mp.info(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + logger_mp.info("Lock all joints except two arms...\n") + + arm_indices = set(member.value for member in G1_23_JointArmIndex) + for id in G1_23_JointIndex: + self.msg.motor_cmd[id].mode = 1 + if id.value in arm_indices: + if self._Is_wrist_motor(id): + self.msg.motor_cmd[id].kp = self.kp_wrist + self.msg.motor_cmd[id].kd = self.kd_wrist + else: + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].q = self.all_motor_q[id] + logger_mp.info("Lock OK!\n") + + # initialize publish thread + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() + + logger_mp.info("Initialize G1_23_ArmController OK!\n") + + def _subscribe_motor_state(self): + while True: + msg = self.lowstate_subscriber.Read() + if msg is not None: + lowstate = G1_23_LowState() + for id in range(G1_23_Num_Motors): + lowstate.motor_state[id].q = msg.motor_state[id].q + lowstate.motor_state[id].dq = msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) + time.sleep(0.002) + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): + if self.motion_mode: + self.msg.motor_cmd[G1_23_JointIndex.kNotUsedJoint0].q = 1.0 + + while True: + start_time = time.time() + + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + if self.simulation_mode: + cliped_arm_q_target = arm_q_target + else: + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit=self.arm_velocity_limit) + + for idx, id in enumerate(G1_23_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # logger_mp.debug(f"arm_velocity_limit:{self.arm_velocity_limit}") + # logger_mp.debug(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + """Set control target values q & tau of the left and right arm motors.""" + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target + + def get_mode_machine(self): + """Return current dds mode machine.""" + return self.lowstate_subscriber.Read().mode_machine + + def get_current_motor_q(self): + """Return current state q of all body motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_23_JointIndex]) + + def get_current_dual_arm_q(self): + """Return current state q of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_23_JointArmIndex]) + + def get_current_dual_arm_dq(self): + """Return current state dq of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in G1_23_JointArmIndex]) + + def ctrl_dual_arm_go_home(self): + """Move both the left and right arms of the robot to their home position by setting the target joint angles (q) and torques (tau) to zero.""" + logger_mp.info("[G1_23_ArmController] ctrl_dual_arm_go_home start...") + max_attempts = 100 + current_attempts = 0 + with self.ctrl_lock: + self.q_target = np.zeros(10) + # self.tauff_target = np.zeros(10) + tolerance = 0.05 # Tolerance threshold for joint angles to determine "close to zero", can be adjusted based on your motor's precision requirements + while current_attempts < max_attempts: + current_q = self.get_current_dual_arm_q() + if np.all(np.abs(current_q) < tolerance): + if self.motion_mode: + for weight in np.linspace(1, 0, num=101): + self.msg.motor_cmd[G1_23_JointIndex.kNotUsedJoint0].q = weight + time.sleep(0.02) + logger_mp.info("[G1_23_ArmController] both arms have reached the home position.") + break + current_attempts += 1 + time.sleep(0.05) + + def speed_gradual_max(self, t=5.0): + """Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.""" + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True + + def speed_instant_max(self): + """set arms velocity to the maximum value immediately, instead of gradually increasing.""" + self.arm_velocity_limit = 30.0 + + def _Is_weak_motor(self, motor_index): + weak_motors = [ + G1_23_JointIndex.kLeftAnklePitch.value, + G1_23_JointIndex.kRightAnklePitch.value, + # Left arm + G1_23_JointIndex.kLeftShoulderPitch.value, + G1_23_JointIndex.kLeftShoulderRoll.value, + G1_23_JointIndex.kLeftShoulderYaw.value, + G1_23_JointIndex.kLeftElbow.value, + # Right arm + G1_23_JointIndex.kRightShoulderPitch.value, + G1_23_JointIndex.kRightShoulderRoll.value, + G1_23_JointIndex.kRightShoulderYaw.value, + G1_23_JointIndex.kRightElbow.value, + ] + return motor_index.value in weak_motors + + def _Is_wrist_motor(self, motor_index): + wrist_motors = [ + G1_23_JointIndex.kLeftWristRoll.value, + G1_23_JointIndex.kRightWristRoll.value, + ] + return motor_index.value in wrist_motors + + +class G1_23_JointArmIndex(IntEnum): + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + + +class G1_23_JointIndex(IntEnum): + # Left leg + kLeftHipPitch = 0 + kLeftHipRoll = 1 + kLeftHipYaw = 2 + kLeftKnee = 3 + kLeftAnklePitch = 4 + kLeftAnkleRoll = 5 + + # Right leg + kRightHipPitch = 6 + kRightHipRoll = 7 + kRightHipYaw = 8 + kRightKnee = 9 + kRightAnklePitch = 10 + kRightAnkleRoll = 11 + + kWaistYaw = 12 + kWaistRollNotUsed = 13 + kWaistPitchNotUsed = 14 + + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitchNotUsed = 20 + kLeftWristyawNotUsed = 21 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitchNotUsed = 27 + kRightWristYawNotUsed = 28 + + # not used + kNotUsedJoint0 = 29 + kNotUsedJoint1 = 30 + kNotUsedJoint2 = 31 + kNotUsedJoint3 = 32 + kNotUsedJoint4 = 33 + kNotUsedJoint5 = 34 + + +class H1_2_ArmController: + def __init__(self, simulation_mode=False): + self.simulation_mode = simulation_mode + + logger_mp.info("Initialize H1_2_ArmController...") + self.q_target = np.zeros(14) + self.tauff_target = np.zeros(14) + + self.kp_high = 300.0 + self.kd_high = 5.0 + self.kp_low = 140.0 + self.kd_low = 3.0 + self.kp_wrist = 50.0 + self.kd_wrist = 2.0 + + self.all_motor_q = None + self.arm_velocity_limit = 20.0 + self.control_dt = 1.0 / 250.0 + + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None + + # initialize lowcmd publisher and lowstate subscriber + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd) + self.lowcmd_publisher.Init() + self.lowstate_subscriber = ChannelSubscriber(kTopicLowState, hg_LowState) + self.lowstate_subscriber.Init() + self.lowstate_buffer = DataBuffer() + + # initialize subscribe thread + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() + + while not self.lowstate_buffer.GetData(): + time.sleep(0.1) + logger_mp.warning("[H1_2_ArmController] Waiting to subscribe dds...") + logger_mp.info("[H1_2_ArmController] Subscribe dds ok.") + + # initialize hg's lowcmd msg + self.crc = CRC() + self.msg = unitree_hg_msg_dds__LowCmd_() + self.msg.mode_pr = 0 + self.msg.mode_machine = self.get_mode_machine() + + self.all_motor_q = self.get_current_motor_q() + logger_mp.info(f"Current all body motor state q:\n{self.all_motor_q} \n") + logger_mp.info(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + logger_mp.info("Lock all joints except two arms...\n") + + arm_indices = set(member.value for member in H1_2_JointArmIndex) + for id in H1_2_JointIndex: + self.msg.motor_cmd[id].mode = 1 + if id.value in arm_indices: + if self._Is_wrist_motor(id): + self.msg.motor_cmd[id].kp = self.kp_wrist + self.msg.motor_cmd[id].kd = self.kd_wrist + else: + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].q = self.all_motor_q[id] + logger_mp.info("Lock OK!\n") + + # initialize publish thread + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() + + logger_mp.info("Initialize H1_2_ArmController OK!\n") + + def _subscribe_motor_state(self): + while True: + msg = self.lowstate_subscriber.Read() + if msg is not None: + lowstate = H1_2_LowState() + for id in range(H1_2_Num_Motors): + lowstate.motor_state[id].q = msg.motor_state[id].q + lowstate.motor_state[id].dq = msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) + time.sleep(0.002) + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): + while True: + start_time = time.time() + + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + if self.simulation_mode: + cliped_arm_q_target = arm_q_target + else: + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit=self.arm_velocity_limit) + + for idx, id in enumerate(H1_2_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # logger_mp.debug(f"arm_velocity_limit:{self.arm_velocity_limit}") + # logger_mp.debug(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + """Set control target values q & tau of the left and right arm motors.""" + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target + + def get_mode_machine(self): + """Return current dds mode machine.""" + return self.lowstate_subscriber.Read().mode_machine + + def get_current_motor_q(self): + """Return current state q of all body motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in H1_2_JointIndex]) + + def get_current_dual_arm_q(self): + """Return current state q of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in H1_2_JointArmIndex]) + + def get_current_dual_arm_dq(self): + """Return current state dq of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in H1_2_JointArmIndex]) + + def ctrl_dual_arm_go_home(self): + """Move both the left and right arms of the robot to their home position by setting the target joint angles (q) and torques (tau) to zero.""" + logger_mp.info("[H1_2_ArmController] ctrl_dual_arm_go_home start...") + max_attempts = 100 + current_attempts = 0 + with self.ctrl_lock: + self.q_target = np.zeros(14) + # self.tauff_target = np.zeros(14) + tolerance = 0.05 # Tolerance threshold for joint angles to determine "close to zero", can be adjusted based on your motor's precision requirements + while current_attempts < max_attempts: + current_q = self.get_current_dual_arm_q() + if np.all(np.abs(current_q) < tolerance): + logger_mp.info("[H1_2_ArmController] both arms have reached the home position.") + break + current_attempts += 1 + time.sleep(0.05) + + def speed_gradual_max(self, t=5.0): + """Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.""" + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True + + def speed_instant_max(self): + """set arms velocity to the maximum value immediately, instead of gradually increasing.""" + self.arm_velocity_limit = 30.0 + + def _Is_weak_motor(self, motor_index): + weak_motors = [ + H1_2_JointIndex.kLeftAnkle.value, + H1_2_JointIndex.kRightAnkle.value, + # Left arm + H1_2_JointIndex.kLeftShoulderPitch.value, + H1_2_JointIndex.kLeftShoulderRoll.value, + H1_2_JointIndex.kLeftShoulderYaw.value, + H1_2_JointIndex.kLeftElbowPitch.value, + # Right arm + H1_2_JointIndex.kRightShoulderPitch.value, + H1_2_JointIndex.kRightShoulderRoll.value, + H1_2_JointIndex.kRightShoulderYaw.value, + H1_2_JointIndex.kRightElbowPitch.value, + ] + return motor_index.value in weak_motors + + def _Is_wrist_motor(self, motor_index): + wrist_motors = [ + H1_2_JointIndex.kLeftElbowRoll.value, + H1_2_JointIndex.kLeftWristPitch.value, + H1_2_JointIndex.kLeftWristyaw.value, + H1_2_JointIndex.kRightElbowRoll.value, + H1_2_JointIndex.kRightWristPitch.value, + H1_2_JointIndex.kRightWristYaw.value, + ] + return motor_index.value in wrist_motors + + +class H1_2_JointArmIndex(IntEnum): + # Left arm + kLeftShoulderPitch = 13 + kLeftShoulderRoll = 14 + kLeftShoulderYaw = 15 + kLeftElbowPitch = 16 + kLeftElbowRoll = 17 + kLeftWristPitch = 18 + kLeftWristyaw = 19 + + # Right arm + kRightShoulderPitch = 20 + kRightShoulderRoll = 21 + kRightShoulderYaw = 22 + kRightElbowPitch = 23 + kRightElbowRoll = 24 + kRightWristPitch = 25 + kRightWristYaw = 26 + + +class H1_2_JointIndex(IntEnum): + # Left leg + kLeftHipYaw = 0 + kLeftHipRoll = 1 + kLeftHipPitch = 2 + kLeftKnee = 3 + kLeftAnkle = 4 + kLeftAnkleRoll = 5 + + # Right leg + kRightHipYaw = 6 + kRightHipRoll = 7 + kRightHipPitch = 8 + kRightKnee = 9 + kRightAnkle = 10 + kRightAnkleRoll = 11 + + kWaistYaw = 12 + + # Left arm + kLeftShoulderPitch = 13 + kLeftShoulderRoll = 14 + kLeftShoulderYaw = 15 + kLeftElbowPitch = 16 + kLeftElbowRoll = 17 + kLeftWristPitch = 18 + kLeftWristyaw = 19 + + # Right arm + kRightShoulderPitch = 20 + kRightShoulderRoll = 21 + kRightShoulderYaw = 22 + kRightElbowPitch = 23 + kRightElbowRoll = 24 + kRightWristPitch = 25 + kRightWristYaw = 26 + + kNotUsedJoint0 = 27 + kNotUsedJoint1 = 28 + kNotUsedJoint2 = 29 + kNotUsedJoint3 = 30 + kNotUsedJoint4 = 31 + kNotUsedJoint5 = 32 + kNotUsedJoint6 = 33 + kNotUsedJoint7 = 34 + + +class H1_ArmController: + def __init__(self, simulation_mode=False): + self.simulation_mode = simulation_mode + + logger_mp.info("Initialize H1_ArmController...") + self.q_target = np.zeros(8) + self.tauff_target = np.zeros(8) + + self.kp_high = 300.0 + self.kd_high = 5.0 + self.kp_low = 140.0 + self.kd_low = 3.0 + + self.all_motor_q = None + self.arm_velocity_limit = 20.0 + self.control_dt = 1.0 / 250.0 + + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None + + # initialize lowcmd publisher and lowstate subscriber + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Debug, go_LowCmd) + self.lowcmd_publisher.Init() + self.lowstate_subscriber = ChannelSubscriber(kTopicLowState, go_LowState) + self.lowstate_subscriber.Init() + self.lowstate_buffer = DataBuffer() + + # initialize subscribe thread + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() + + while not self.lowstate_buffer.GetData(): + time.sleep(0.1) + logger_mp.warning("[H1_ArmController] Waiting to subscribe dds...") + logger_mp.info("[H1_ArmController] Subscribe dds ok.") + + # initialize h1's lowcmd msg + self.crc = CRC() + self.msg = unitree_go_msg_dds__LowCmd_() + self.msg.head[0] = 0xFE + self.msg.head[1] = 0xEF + self.msg.level_flag = 0xFF + self.msg.gpio = 0 + + self.all_motor_q = self.get_current_motor_q() + logger_mp.info(f"Current all body motor state q:\n{self.all_motor_q} \n") + logger_mp.info(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + logger_mp.info("Lock all joints except two arms...\n") + + for id in H1_JointIndex: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + self.msg.motor_cmd[id].mode = 0x01 + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].mode = 0x0A + self.msg.motor_cmd[id].q = self.all_motor_q[id] + logger_mp.info("Lock OK!\n") + + # initialize publish thread + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() + + logger_mp.info("Initialize H1_ArmController OK!\n") + + def _subscribe_motor_state(self): + while True: + msg = self.lowstate_subscriber.Read() + if msg is not None: + lowstate = H1_LowState() + for id in range(H1_Num_Motors): + lowstate.motor_state[id].q = msg.motor_state[id].q + lowstate.motor_state[id].dq = msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) + time.sleep(0.002) + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): + while True: + start_time = time.time() + + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + if self.simulation_mode: + cliped_arm_q_target = arm_q_target + else: + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit=self.arm_velocity_limit) + + for idx, id in enumerate(H1_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # logger_mp.debug(f"arm_velocity_limit:{self.arm_velocity_limit}") + # logger_mp.debug(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + """Set control target values q & tau of the left and right arm motors.""" + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target + + def get_current_motor_q(self): + """Return current state q of all body motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in H1_JointIndex]) + + def get_current_dual_arm_q(self): + """Return current state q of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in H1_JointArmIndex]) + + def get_current_dual_arm_dq(self): + """Return current state dq of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in H1_JointArmIndex]) + + def ctrl_dual_arm_go_home(self): + """Move both the left and right arms of the robot to their home position by setting the target joint angles (q) and torques (tau) to zero.""" + logger_mp.info("[H1_ArmController] ctrl_dual_arm_go_home start...") + max_attempts = 100 + current_attempts = 0 + with self.ctrl_lock: + self.q_target = np.zeros(8) + # self.tauff_target = np.zeros(8) + tolerance = 0.05 # Tolerance threshold for joint angles to determine "close to zero", can be adjusted based on your motor's precision requirements + while current_attempts < max_attempts: + current_q = self.get_current_dual_arm_q() + if np.all(np.abs(current_q) < tolerance): + logger_mp.info("[H1_ArmController] both arms have reached the home position.") + break + current_attempts += 1 + time.sleep(0.05) + + def speed_gradual_max(self, t=5.0): + """Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.""" + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True + + def speed_instant_max(self): + """set arms velocity to the maximum value immediately, instead of gradually increasing.""" + self.arm_velocity_limit = 30.0 + + def _Is_weak_motor(self, motor_index): + weak_motors = [ + H1_JointIndex.kLeftAnkle.value, + H1_JointIndex.kRightAnkle.value, + # Left arm + H1_JointIndex.kLeftShoulderPitch.value, + H1_JointIndex.kLeftShoulderRoll.value, + H1_JointIndex.kLeftShoulderYaw.value, + H1_JointIndex.kLeftElbow.value, + # Right arm + H1_JointIndex.kRightShoulderPitch.value, + H1_JointIndex.kRightShoulderRoll.value, + H1_JointIndex.kRightShoulderYaw.value, + H1_JointIndex.kRightElbow.value, + ] + return motor_index.value in weak_motors + + +class H1_JointArmIndex(IntEnum): + # Unlike G1 and H1_2, the arm order in DDS messages for H1 is right then left. + # Therefore, the purpose of switching the order here is to maintain consistency with G1 and H1_2. + # Left arm + kLeftShoulderPitch = 16 + kLeftShoulderRoll = 17 + kLeftShoulderYaw = 18 + kLeftElbow = 19 + # Right arm + kRightShoulderPitch = 12 + kRightShoulderRoll = 13 + kRightShoulderYaw = 14 + kRightElbow = 15 + + +class H1_JointIndex(IntEnum): + kRightHipRoll = 0 + kRightHipPitch = 1 + kRightKnee = 2 + kLeftHipRoll = 3 + kLeftHipPitch = 4 + kLeftKnee = 5 + kWaistYaw = 6 + kLeftHipYaw = 7 + kRightHipYaw = 8 + kNotUsedJoint = 9 + kLeftAnkle = 10 + kRightAnkle = 11 + # Right arm + kRightShoulderPitch = 12 + kRightShoulderRoll = 13 + kRightShoulderYaw = 14 + kRightElbow = 15 + # Left arm + kLeftShoulderPitch = 16 + kLeftShoulderRoll = 17 + kLeftShoulderYaw = 18 + kLeftElbow = 19 + + +if __name__ == "__main__": + from robot_arm_ik import G1_29_ArmIK + import pinocchio as pin + + arm_ik = G1_29_ArmIK(Unit_Test=True, Visualization=False) + arm = G1_29_ArmController(simulation_mode=True) + # arm_ik = G1_23_ArmIK(Unit_Test = True, Visualization = False) + # arm = G1_23_ArmController() + # arm_ik = H1_2_ArmIK(Unit_Test = True, Visualization = False) + # arm = H1_2_ArmController() + # arm_ik = H1_ArmIK(Unit_Test = True, Visualization = True) + # arm = H1_ArmController() + + # initial positon + L_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, +0.25, 0.1]), + ) + + R_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, -0.25, 0.1]), + ) + + rotation_speed = 0.005 # Rotation speed in radians per iteration + + user_input = input("Please enter the start signal (enter 's' to start the subsequent program): \n") + if user_input.lower() == "s": + step = 0 + arm.speed_gradual_max() + while True: + if step <= 120: + angle = rotation_speed * step + L_quat = pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) # y axis + R_quat = pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) # z axis + + L_tf_target.translation += np.array([0.001, 0.001, 0.001]) + R_tf_target.translation += np.array([0.001, -0.001, 0.001]) + else: + angle = rotation_speed * (240 - step) + L_quat = pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) # y axis + R_quat = pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) # z axis + + L_tf_target.translation -= np.array([0.001, 0.001, 0.001]) + R_tf_target.translation -= np.array([0.001, -0.001, 0.001]) + + L_tf_target.rotation = L_quat.toRotationMatrix() + R_tf_target.rotation = R_quat.toRotationMatrix() + + current_lr_arm_q = arm.get_current_dual_arm_q() + current_lr_arm_dq = arm.get_current_dual_arm_dq() + + sol_q, sol_tauff = arm_ik.solve_ik( + L_tf_target.homogeneous, R_tf_target.homogeneous, current_lr_arm_q, current_lr_arm_dq + ) + + arm.ctrl_dual_arm(sol_q, sol_tauff) + + step += 1 + if step > 240: + step = 0 + time.sleep(0.01) diff --git a/eval_robot/robot_control/robot_arm_ik.py b/eval_robot/robot_control/robot_arm_ik.py new file mode 100644 index 000000000..8392fe12f --- /dev/null +++ b/eval_robot/robot_control/robot_arm_ik.py @@ -0,0 +1,1148 @@ +import casadi +import meshcat.geometry as mg +import numpy as np +import pinocchio as pin +import time +from pinocchio import casadi as cpin +from pinocchio.visualize import MeshcatVisualizer +import os +import sys +from lerobot.robots.unitree_g1.eval_robot.utils.weighted_moving_filter import WeightedMovingFilter + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) +parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(parent2_dir) + + +class G1_29_ArmIK: + def __init__(self, Unit_Test=False, Visualization=False): + np.set_printoptions(precision=5, suppress=True, linewidth=200) + + self.Unit_Test = Unit_Test + self.Visualization = Visualization + + if not self.Unit_Test: + self.robot = pin.RobotWrapper.BuildFromURDF( + "src/lerobot/robots/unitree_g1/eval_robot/assets/g1/g1_body29_hand14.urdf", "src/lerobot/robots/unitree_g1/eval_robot/assets/g1/" + ) + else: + self.robot = pin.RobotWrapper.BuildFromURDF( + "src/lerobot/robots/unitree_g1/eval_robot/assets/g1/g1_body29_hand14.urdf", "src/lerobot/robots/unitree_g1/eval_robot/assets/g1/" + ) # for test + + self.mixed_jointsToLockIDs = [ + "left_hip_pitch_joint", + "left_hip_roll_joint", + "left_hip_yaw_joint", + "left_knee_joint", + "left_ankle_pitch_joint", + "left_ankle_roll_joint", + "right_hip_pitch_joint", + "right_hip_roll_joint", + "right_hip_yaw_joint", + "right_knee_joint", + "right_ankle_pitch_joint", + "right_ankle_roll_joint", + "waist_yaw_joint", + "waist_roll_joint", + "waist_pitch_joint", + "left_hand_thumb_0_joint", + "left_hand_thumb_1_joint", + "left_hand_thumb_2_joint", + "left_hand_middle_0_joint", + "left_hand_middle_1_joint", + "left_hand_index_0_joint", + "left_hand_index_1_joint", + "right_hand_thumb_0_joint", + "right_hand_thumb_1_joint", + "right_hand_thumb_2_joint", + "right_hand_index_0_joint", + "right_hand_index_1_joint", + "right_hand_middle_0_joint", + "right_hand_middle_1_joint", + ] + + self.reduced_robot = self.robot.buildReducedRobot( + list_of_joints_to_lock=self.mixed_jointsToLockIDs, + reference_configuration=np.array([0.0] * self.robot.model.nq), + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "L_ee", + self.reduced_robot.model.getJointId("left_wrist_yaw_joint"), + pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "R_ee", + self.reduced_robot.model.getJointId("right_wrist_yaw_joint"), + pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + # for i in range(self.reduced_robot.model.nframes): + # frame = self.reduced_robot.model.frames[i] + # frame_id = self.reduced_robot.model.getFrameId(frame.name) + # logger_mp.debug(f"Frame ID: {frame_id}, Name: {frame.name}") + # for idx, name in enumerate(self.reduced_robot.model.names): + # logger_mp.debug(f"{idx}: {name}") + + # Creating Casadi models and data for symbolic computing + self.cmodel = cpin.Model(self.reduced_robot.model) + self.cdata = self.cmodel.createData() + + # Creating symbolic variables + self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1) + self.cTf_l = casadi.SX.sym("tf_l", 4, 4) + self.cTf_r = casadi.SX.sym("tf_r", 4, 4) + cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq) + + # Get the hand joint ID and define the error function + self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") + self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3], + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T), + ) + ], + ) + + # Defining the optimization problem + self.opti = casadi.Opti() + self.var_q = self.opti.variable(self.reduced_robot.model.nq) + self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth + self.param_tf_l = self.opti.parameter(4, 4) + self.param_tf_r = self.opti.parameter(4, 4) + self.translational_cost = casadi.sumsqr(self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.rotation_cost = casadi.sumsqr(self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.regularization_cost = casadi.sumsqr(self.var_q) + self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) + + # Setting optimization constraints and goals + self.opti.subject_to( + self.opti.bounded( + self.reduced_robot.model.lowerPositionLimit, self.var_q, self.reduced_robot.model.upperPositionLimit + ) + ) + self.opti.minimize( + 50 * self.translational_cost + self.rotation_cost + 0.02 * self.regularization_cost + 0.1 * self.smooth_cost + ) + + opts = { + "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6}, + "print_time": False, # print or not + "calc_lam_p": False, # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F + } + self.opti.solver("ipopt", opts) + + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14) + self.vis = None + + if self.Visualization: + # Initialize the Meshcat visualizer for visualization + self.vis = MeshcatVisualizer( + self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model + ) + self.vis.initViewer(open=True) + self.vis.loadViewerModel("pinocchio") + self.vis.displayFrames(True, frame_ids=[107, 108], axis_length=0.15, axis_width=5) + self.vis.display(pin.neutral(self.reduced_robot.model)) + + # Enable the display of end effector target frames with short axis lengths and greater width. + frame_viz_names = ["L_ee_target", "R_ee_target"] + FRAME_AXIS_POSITIONS = ( + np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 1]]).astype(np.float32).T + ) + FRAME_AXIS_COLORS = ( + np.array([[1, 0, 0], [1, 0.6, 0], [0, 1, 0], [0.6, 1, 0], [0, 0, 1], [0, 0.6, 1]]).astype(np.float32).T + ) + axis_length = 0.1 + axis_width = 20 + for frame_viz_name in frame_viz_names: + self.vis.viewer[frame_viz_name].set_object( + mg.LineSegments( + mg.PointsGeometry( + position=axis_length * FRAME_AXIS_POSITIONS, + color=FRAME_AXIS_COLORS, + ), + mg.LineBasicMaterial( + linewidth=axis_width, + vertexColors=True, + ), + ) + ) + + # If the robot arm is not the same size as your arm :) + def scale_arms(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): + scale_factor = robot_arm_length / human_arm_length + robot_left_pose = human_left_pose.copy() + robot_right_pose = human_right_pose.copy() + robot_left_pose[:3, 3] *= scale_factor + robot_right_pose[:3, 3] *= scale_factor + return robot_left_pose, robot_right_pose + + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q + self.opti.set_initial(self.var_q, self.init_data) + + # left_wrist, right_wrist = self.scale_arms(left_wrist, right_wrist) + if self.Visualization: + self.vis.viewer["L_ee_target"].set_transform(left_wrist) # for visualization + self.vis.viewer["R_ee_target"].set_transform(right_wrist) # for visualization + + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) + self.opti.set_value(self.var_q_last, self.init_data) # for smooth + + try: + sol = self.opti.solve() + # sol = self.opti.solve_limited() + + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + if self.Visualization: + self.vis.display(sol_q) # for visualization + + return sol_q, sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + logger_mp.error( + f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}" + ) + if self.Visualization: + self.vis.display(sol_q) # for visualization + + # return sol_q, sol_tauff + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) + + def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + try: + sol_tauff = pin.rnea( + self.reduced_robot.model, + self.reduced_robot.data, + current_lr_arm_motor_q, + np.zeros(14), + np.zeros(self.reduced_robot.model.nv), + ) + return sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + return np.zeros(self.reduced_robot.model.nv) + + +class G1_23_ArmIK: + def __init__(self, Unit_Test=False, Visualization=False): + np.set_printoptions(precision=5, suppress=True, linewidth=200) + + self.Unit_Test = Unit_Test + self.Visualization = Visualization + + if not self.Unit_Test: + self.robot = pin.RobotWrapper.BuildFromURDF( + "unitree_lerobot/eval_robot/assets/g1/g1_body23.urdf", "unitree_lerobot/eval_robot/assets/g1/" + ) + else: + self.robot = pin.RobotWrapper.BuildFromURDF( + "unitree_lerobot/eval_robot/assets/g1/g1_body23.urdf", "unitree_lerobot/eval_robot/assets/g1/" + ) # for test + + self.mixed_jointsToLockIDs = [ + "left_hip_pitch_joint", + "left_hip_roll_joint", + "left_hip_yaw_joint", + "left_knee_joint", + "left_ankle_pitch_joint", + "left_ankle_roll_joint", + "right_hip_pitch_joint", + "right_hip_roll_joint", + "right_hip_yaw_joint", + "right_knee_joint", + "right_ankle_pitch_joint", + "right_ankle_roll_joint", + "waist_yaw_joint", + ] + + self.reduced_robot = self.robot.buildReducedRobot( + list_of_joints_to_lock=self.mixed_jointsToLockIDs, + reference_configuration=np.array([0.0] * self.robot.model.nq), + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "L_ee", + self.reduced_robot.model.getJointId("left_wrist_roll_joint"), + pin.SE3(np.eye(3), np.array([0.20, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "R_ee", + self.reduced_robot.model.getJointId("right_wrist_roll_joint"), + pin.SE3(np.eye(3), np.array([0.20, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + # for i in range(self.reduced_robot.model.nframes): + # frame = self.reduced_robot.model.frames[i] + # frame_id = self.reduced_robot.model.getFrameId(frame.name) + # logger_mp.debug(f"Frame ID: {frame_id}, Name: {frame.name}") + + # Creating Casadi models and data for symbolic computing + self.cmodel = cpin.Model(self.reduced_robot.model) + self.cdata = self.cmodel.createData() + + # Creating symbolic variables + self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1) + self.cTf_l = casadi.SX.sym("tf_l", 4, 4) + self.cTf_r = casadi.SX.sym("tf_r", 4, 4) + cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq) + + # Get the hand joint ID and define the error function + self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") + self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3], + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T), + ) + ], + ) + + # Defining the optimization problem + self.opti = casadi.Opti() + self.var_q = self.opti.variable(self.reduced_robot.model.nq) + self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth + self.param_tf_l = self.opti.parameter(4, 4) + self.param_tf_r = self.opti.parameter(4, 4) + self.translational_cost = casadi.sumsqr(self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.rotation_cost = casadi.sumsqr(self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.regularization_cost = casadi.sumsqr(self.var_q) + self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) + + # Setting optimization constraints and goals + self.opti.subject_to( + self.opti.bounded( + self.reduced_robot.model.lowerPositionLimit, self.var_q, self.reduced_robot.model.upperPositionLimit + ) + ) + self.opti.minimize( + 50 * self.translational_cost + + 0.5 * self.rotation_cost + + 0.02 * self.regularization_cost + + 0.1 * self.smooth_cost + ) + + opts = { + "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6}, + "print_time": False, # print or not + "calc_lam_p": False, # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F + } + self.opti.solver("ipopt", opts) + + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 10) + self.vis = None + + if self.Visualization: + # Initialize the Meshcat visualizer for visualization + self.vis = MeshcatVisualizer( + self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model + ) + self.vis.initViewer(open=True) + self.vis.loadViewerModel("pinocchio") + self.vis.displayFrames(True, frame_ids=[67, 68], axis_length=0.15, axis_width=5) + self.vis.display(pin.neutral(self.reduced_robot.model)) + + # Enable the display of end effector target frames with short axis lengths and greater width. + frame_viz_names = ["L_ee_target", "R_ee_target"] + FRAME_AXIS_POSITIONS = ( + np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 1]]).astype(np.float32).T + ) + FRAME_AXIS_COLORS = ( + np.array([[1, 0, 0], [1, 0.6, 0], [0, 1, 0], [0.6, 1, 0], [0, 0, 1], [0, 0.6, 1]]).astype(np.float32).T + ) + axis_length = 0.1 + axis_width = 20 + for frame_viz_name in frame_viz_names: + self.vis.viewer[frame_viz_name].set_object( + mg.LineSegments( + mg.PointsGeometry( + position=axis_length * FRAME_AXIS_POSITIONS, + color=FRAME_AXIS_COLORS, + ), + mg.LineBasicMaterial( + linewidth=axis_width, + vertexColors=True, + ), + ) + ) + + # If the robot arm is not the same size as your arm :) + def scale_arms(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): + scale_factor = robot_arm_length / human_arm_length + robot_left_pose = human_left_pose.copy() + robot_right_pose = human_right_pose.copy() + robot_left_pose[:3, 3] *= scale_factor + robot_right_pose[:3, 3] *= scale_factor + return robot_left_pose, robot_right_pose + + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q + self.opti.set_initial(self.var_q, self.init_data) + + # left_wrist, right_wrist = self.scale_arms(left_wrist, right_wrist) + if self.Visualization: + self.vis.viewer["L_ee_target"].set_transform(left_wrist) # for visualization + self.vis.viewer["R_ee_target"].set_transform(right_wrist) # for visualization + + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) + self.opti.set_value(self.var_q_last, self.init_data) # for smooth + + try: + sol = self.opti.solve() + # sol = self.opti.solve_limited() + + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + if self.Visualization: + self.vis.display(sol_q) # for visualization + + return sol_q, sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + logger_mp.error( + f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}" + ) + if self.Visualization: + self.vis.display(sol_q) # for visualization + + # return sol_q, sol_tauff + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) + + def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + try: + sol_tauff = pin.rnea( + self.reduced_robot.model, + self.reduced_robot.data, + current_lr_arm_motor_q, + np.zeros(14), + np.zeros(self.reduced_robot.model.nv), + ) + return sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + return np.zeros(self.reduced_robot.model.nv) + + +class H1_2_ArmIK: + def __init__(self, Unit_Test=False, Visualization=False): + np.set_printoptions(precision=5, suppress=True, linewidth=200) + + self.Unit_Test = Unit_Test + self.Visualization = Visualization + + if not self.Unit_Test: + self.robot = pin.RobotWrapper.BuildFromURDF( + "unitree_lerobot/eval_robot/assets/h1_2/h1_2.urdf", "unitree_lerobot/eval_robot/assets/h1_2/" + ) + else: + self.robot = pin.RobotWrapper.BuildFromURDF( + "unitree_lerobot/eval_robot/assets/h1_2/h1_2.urdf", "unitree_lerobot/eval_robot/assets/h1_2/" + ) # for test + + self.mixed_jointsToLockIDs = [ + "left_hip_yaw_joint", + "left_hip_pitch_joint", + "left_hip_roll_joint", + "left_knee_joint", + "left_ankle_pitch_joint", + "left_ankle_roll_joint", + "right_hip_yaw_joint", + "right_hip_pitch_joint", + "right_hip_roll_joint", + "right_knee_joint", + "right_ankle_pitch_joint", + "right_ankle_roll_joint", + "torso_joint", + "L_index_proximal_joint", + "L_index_intermediate_joint", + "L_middle_proximal_joint", + "L_middle_intermediate_joint", + "L_pinky_proximal_joint", + "L_pinky_intermediate_joint", + "L_ring_proximal_joint", + "L_ring_intermediate_joint", + "L_thumb_proximal_yaw_joint", + "L_thumb_proximal_pitch_joint", + "L_thumb_intermediate_joint", + "L_thumb_distal_joint", + "R_index_proximal_joint", + "R_index_intermediate_joint", + "R_middle_proximal_joint", + "R_middle_intermediate_joint", + "R_pinky_proximal_joint", + "R_pinky_intermediate_joint", + "R_ring_proximal_joint", + "R_ring_intermediate_joint", + "R_thumb_proximal_yaw_joint", + "R_thumb_proximal_pitch_joint", + "R_thumb_intermediate_joint", + "R_thumb_distal_joint", + ] + + self.reduced_robot = self.robot.buildReducedRobot( + list_of_joints_to_lock=self.mixed_jointsToLockIDs, + reference_configuration=np.array([0.0] * self.robot.model.nq), + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "L_ee", + self.reduced_robot.model.getJointId("left_wrist_yaw_joint"), + pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "R_ee", + self.reduced_robot.model.getJointId("right_wrist_yaw_joint"), + pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + # for i in range(self.reduced_robot.model.nframes): + # frame = self.reduced_robot.model.frames[i] + # frame_id = self.reduced_robot.model.getFrameId(frame.name) + # logger_mp.debug(f"Frame ID: {frame_id}, Name: {frame.name}") + + # Creating Casadi models and data for symbolic computing + self.cmodel = cpin.Model(self.reduced_robot.model) + self.cdata = self.cmodel.createData() + + # Creating symbolic variables + self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1) + self.cTf_l = casadi.SX.sym("tf_l", 4, 4) + self.cTf_r = casadi.SX.sym("tf_r", 4, 4) + cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq) + + # Get the hand joint ID and define the error function + self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") + self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3], + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T), + ) + ], + ) + + # Defining the optimization problem + self.opti = casadi.Opti() + self.var_q = self.opti.variable(self.reduced_robot.model.nq) + self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth + self.param_tf_l = self.opti.parameter(4, 4) + self.param_tf_r = self.opti.parameter(4, 4) + self.translational_cost = casadi.sumsqr(self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.rotation_cost = casadi.sumsqr(self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.regularization_cost = casadi.sumsqr(self.var_q) + self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) + + # Setting optimization constraints and goals + self.opti.subject_to( + self.opti.bounded( + self.reduced_robot.model.lowerPositionLimit, self.var_q, self.reduced_robot.model.upperPositionLimit + ) + ) + self.opti.minimize( + 50 * self.translational_cost + self.rotation_cost + 0.02 * self.regularization_cost + 0.1 * self.smooth_cost + ) + + opts = { + "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6}, + "print_time": False, # print or not + "calc_lam_p": False, # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F + } + self.opti.solver("ipopt", opts) + + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14) + self.vis = None + + if self.Visualization: + # Initialize the Meshcat visualizer for visualization + self.vis = MeshcatVisualizer( + self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model + ) + self.vis.initViewer(open=True) + self.vis.loadViewerModel("pinocchio") + self.vis.displayFrames(True, frame_ids=[113, 114], axis_length=0.15, axis_width=5) + self.vis.display(pin.neutral(self.reduced_robot.model)) + + # Enable the display of end effector target frames with short axis lengths and greater width. + frame_viz_names = ["L_ee_target", "R_ee_target"] + FRAME_AXIS_POSITIONS = ( + np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 1]]).astype(np.float32).T + ) + FRAME_AXIS_COLORS = ( + np.array([[1, 0, 0], [1, 0.6, 0], [0, 1, 0], [0.6, 1, 0], [0, 0, 1], [0, 0.6, 1]]).astype(np.float32).T + ) + axis_length = 0.1 + axis_width = 10 + for frame_viz_name in frame_viz_names: + self.vis.viewer[frame_viz_name].set_object( + mg.LineSegments( + mg.PointsGeometry( + position=axis_length * FRAME_AXIS_POSITIONS, + color=FRAME_AXIS_COLORS, + ), + mg.LineBasicMaterial( + linewidth=axis_width, + vertexColors=True, + ), + ) + ) + + # If the robot arm is not the same size as your arm :) + def scale_arms(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): + scale_factor = robot_arm_length / human_arm_length + robot_left_pose = human_left_pose.copy() + robot_right_pose = human_right_pose.copy() + robot_left_pose[:3, 3] *= scale_factor + robot_right_pose[:3, 3] *= scale_factor + return robot_left_pose, robot_right_pose + + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q + self.opti.set_initial(self.var_q, self.init_data) + + left_wrist, right_wrist = self.scale_arms(left_wrist, right_wrist) + if self.Visualization: + self.vis.viewer["L_ee_target"].set_transform(left_wrist) # for visualization + self.vis.viewer["R_ee_target"].set_transform(right_wrist) # for visualization + + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) + self.opti.set_value(self.var_q_last, self.init_data) # for smooth + + try: + sol = self.opti.solve() + # sol = self.opti.solve_limited() + + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + if self.Visualization: + self.vis.display(sol_q) # for visualization + + return sol_q, sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + logger_mp.error( + f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}" + ) + if self.Visualization: + self.vis.display(sol_q) # for visualization + + # return sol_q, sol_tauff + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) + + +class H1_ArmIK: + def __init__(self, Unit_Test=False, Visualization=False): + np.set_printoptions(precision=5, suppress=True, linewidth=200) + + self.Unit_Test = Unit_Test + self.Visualization = Visualization + + if not self.Unit_Test: + self.robot = pin.RobotWrapper.BuildFromURDF("../assets/h1/h1_with_hand.urdf", "../assets/h1/") + else: + self.robot = pin.RobotWrapper.BuildFromURDF( + "../../assets/h1/h1_with_hand.urdf", "../../assets/h1/" + ) # for test + + self.mixed_jointsToLockIDs = [ + "right_hip_roll_joint", + "right_hip_pitch_joint", + "right_knee_joint", + "left_hip_roll_joint", + "left_hip_pitch_joint", + "left_knee_joint", + "torso_joint", + "left_hip_yaw_joint", + "right_hip_yaw_joint", + "left_ankle_joint", + "right_ankle_joint", + "L_index_proximal_joint", + "L_index_intermediate_joint", + "L_middle_proximal_joint", + "L_middle_intermediate_joint", + "L_ring_proximal_joint", + "L_ring_intermediate_joint", + "L_pinky_proximal_joint", + "L_pinky_intermediate_joint", + "L_thumb_proximal_yaw_joint", + "L_thumb_proximal_pitch_joint", + "L_thumb_intermediate_joint", + "L_thumb_distal_joint", + "R_index_proximal_joint", + "R_index_intermediate_joint", + "R_middle_proximal_joint", + "R_middle_intermediate_joint", + "R_ring_proximal_joint", + "R_ring_intermediate_joint", + "R_pinky_proximal_joint", + "R_pinky_intermediate_joint", + "R_thumb_proximal_yaw_joint", + "R_thumb_proximal_pitch_joint", + "R_thumb_intermediate_joint", + "R_thumb_distal_joint", + "left_hand_joint", + "right_hand_joint", + ] + + self.reduced_robot = self.robot.buildReducedRobot( + list_of_joints_to_lock=self.mixed_jointsToLockIDs, + reference_configuration=np.array([0.0] * self.robot.model.nq), + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "L_ee", + self.reduced_robot.model.getJointId("left_elbow_joint"), + pin.SE3(np.eye(3), np.array([0.2605 + 0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + self.reduced_robot.model.addFrame( + pin.Frame( + "R_ee", + self.reduced_robot.model.getJointId("right_elbow_joint"), + pin.SE3(np.eye(3), np.array([0.2605 + 0.05, 0, 0]).T), + pin.FrameType.OP_FRAME, + ) + ) + + # for i in range(self.reduced_robot.model.nframes): + # frame = self.reduced_robot.model.frames[i] + # frame_id = self.reduced_robot.model.getFrameId(frame.name) + # logger_mp.debug(f"Frame ID: {frame_id}, Name: {frame.name}") + + # Creating Casadi models and data for symbolic computing + self.cmodel = cpin.Model(self.reduced_robot.model) + self.cdata = self.cmodel.createData() + + # Creating symbolic variables + self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1) + self.cTf_l = casadi.SX.sym("tf_l", 4, 4) + self.cTf_r = casadi.SX.sym("tf_r", 4, 4) + cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq) + + # Get the hand joint ID and define the error function + self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") + self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3], + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T), + ) + ], + ) + + # Defining the optimization problem + self.opti = casadi.Opti() + self.var_q = self.opti.variable(self.reduced_robot.model.nq) + self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth + self.param_tf_l = self.opti.parameter(4, 4) + self.param_tf_r = self.opti.parameter(4, 4) + self.translational_cost = casadi.sumsqr(self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.rotation_cost = casadi.sumsqr(self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.regularization_cost = casadi.sumsqr(self.var_q) + self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) + + # Setting optimization constraints and goals + self.opti.subject_to( + self.opti.bounded( + self.reduced_robot.model.lowerPositionLimit, self.var_q, self.reduced_robot.model.upperPositionLimit + ) + ) + self.opti.minimize( + 50 * self.translational_cost + + 0.5 * self.rotation_cost + + 0.02 * self.regularization_cost + + 0.1 * self.smooth_cost + ) + + opts = { + "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6}, + "print_time": False, # print or not + "calc_lam_p": False, # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F + } + self.opti.solver("ipopt", opts) + + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 8) + self.vis = None + + if self.Visualization: + # Initialize the Meshcat visualizer for visualization + self.vis = MeshcatVisualizer( + self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model + ) + self.vis.initViewer(open=True) + self.vis.loadViewerModel("pinocchio") + self.vis.displayFrames(True, frame_ids=[105, 106], axis_length=0.15, axis_width=5) + self.vis.display(pin.neutral(self.reduced_robot.model)) + + # Enable the display of end effector target frames with short axis lengths and greater width. + frame_viz_names = ["L_ee_target", "R_ee_target"] + FRAME_AXIS_POSITIONS = ( + np.array([[0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 1]]).astype(np.float32).T + ) + FRAME_AXIS_COLORS = ( + np.array( + [ + [1.0, 0.3, 0.3], + [1.0, 0.7, 0.7], + [0.3, 1.0, 0.5], + [0.7, 1.0, 0.8], + [0.3, 0.8, 1.0], + [0.7, 0.9, 1.0], + ] + ) + .astype(np.float32) + .T + ) + axis_length = 0.1 + axis_width = 10 + for frame_viz_name in frame_viz_names: + self.vis.viewer[frame_viz_name].set_object( + mg.LineSegments( + mg.PointsGeometry( + position=axis_length * FRAME_AXIS_POSITIONS, + color=FRAME_AXIS_COLORS, + ), + mg.LineBasicMaterial( + linewidth=axis_width, + vertexColors=True, + ), + ) + ) + + # If the robot arm is not the same size as your arm :) + def scale_arms(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): + scale_factor = robot_arm_length / human_arm_length + robot_left_pose = human_left_pose.copy() + robot_right_pose = human_right_pose.copy() + robot_left_pose[:3, 3] *= scale_factor + robot_right_pose[:3, 3] *= scale_factor + return robot_left_pose, robot_right_pose + + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q + self.opti.set_initial(self.var_q, self.init_data) + + left_wrist, right_wrist = self.scale_arms(left_wrist, right_wrist) + if self.Visualization: + self.vis.viewer["L_ee_target"].set_transform(left_wrist) # for visualization + self.vis.viewer["R_ee_target"].set_transform(right_wrist) # for visualization + + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) + self.opti.set_value(self.var_q_last, self.init_data) # for smooth + + try: + sol = self.opti.solve() + # sol = self.opti.solve_limited() + + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + if self.Visualization: + self.vis.display(sol_q) # for visualization + + return sol_q, sol_tauff + + except Exception as e: + logger_mp.error(f"ERROR in convergence, plotting debug info.{e}") + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea( + self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv) + ) + + logger_mp.error( + f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}" + ) + if self.Visualization: + self.vis.display(sol_q) # for visualization + + # return sol_q, sol_tauff + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) + + +if __name__ == "__main__": + arm_ik = G1_29_ArmIK(Unit_Test=True, Visualization=True) + # arm_ik = H1_2_ArmIK(Unit_Test = True, Visualization = True) + # arm_ik = G1_23_ArmIK(Unit_Test = True, Visualization = True) + # arm_ik = H1_ArmIK(Unit_Test = True, Visualization = True) + + # initial positon + L_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, +0.25, 0.1]), + ) + + R_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, -0.25, 0.1]), + ) + + rotation_speed = 0.005 + noise_amplitude_translation = 0.001 + noise_amplitude_rotation = 0.01 + + user_input = input("Please enter the start signal (enter 's' to start the subsequent program):\n") + if user_input.lower() == "s": + step = 0 + while True: + # Apply rotation noise with bias towards y and z axes + rotation_noise_L = pin.Quaternion( + np.cos(np.random.normal(0, noise_amplitude_rotation) / 2), + 0, + np.random.normal(0, noise_amplitude_rotation / 2), + 0, + ).normalized() # y bias + + rotation_noise_R = pin.Quaternion( + np.cos(np.random.normal(0, noise_amplitude_rotation) / 2), + 0, + 0, + np.random.normal(0, noise_amplitude_rotation / 2), + ).normalized() # z bias + + if step <= 120: + angle = rotation_speed * step + L_tf_target.rotation = ( + rotation_noise_L * pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) + ).toRotationMatrix() # y axis + R_tf_target.rotation = ( + rotation_noise_R * pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) + ).toRotationMatrix() # z axis + L_tf_target.translation += np.array([0.001, 0.001, 0.001]) + np.random.normal( + 0, noise_amplitude_translation, 3 + ) + R_tf_target.translation += np.array([0.001, -0.001, 0.001]) + np.random.normal( + 0, noise_amplitude_translation, 3 + ) + else: + angle = rotation_speed * (240 - step) + L_tf_target.rotation = ( + rotation_noise_L * pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) + ).toRotationMatrix() # y axis + R_tf_target.rotation = ( + rotation_noise_R * pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) + ).toRotationMatrix() # z axis + L_tf_target.translation -= np.array([0.001, 0.001, 0.001]) + np.random.normal( + 0, noise_amplitude_translation, 3 + ) + R_tf_target.translation -= np.array([0.001, -0.001, 0.001]) + np.random.normal( + 0, noise_amplitude_translation, 3 + ) + + arm_ik.solve_ik(L_tf_target.homogeneous, R_tf_target.homogeneous) + + step += 1 + if step > 240: + step = 0 + time.sleep(0.1) diff --git a/eval_robot/robot_control/robot_arm_test.py b/eval_robot/robot_control/robot_arm_test.py new file mode 100644 index 000000000..cfbcf3ca6 --- /dev/null +++ b/eval_robot/robot_control/robot_arm_test.py @@ -0,0 +1,343 @@ +import numpy as np +import threading +import time +from enum import IntEnum + +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as hg_LowCmd, LowState_ as hg_LowState # idl for g1, h1_2 +from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_ +from unitree_sdk2py.utils.crc import CRC + +from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowCmd_ as go_LowCmd, LowState_ as go_LowState # idl for h1 +from unitree_sdk2py.idl.default import unitree_go_msg_dds__LowCmd_ + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + +kTopicLowCommand_Debug = "rt/lowcmd" +kTopicLowCommand_Motion = "rt/arm_sdk" +kTopicLowState = "rt/lowstate" + +G1_29_Num_Motors = 35 +G1_23_Num_Motors = 35 +H1_2_Num_Motors = 35 +H1_Num_Motors = 20 + + +class MotorState: + def __init__(self): + self.q = None + self.dq = None + + +class G1_29_LowState: + def __init__(self): + self.motor_state = [MotorState() for _ in range(G1_29_Num_Motors)] + +class DataBuffer: + def __init__(self): + self.data = None + self.lock = threading.Lock() + + def GetData(self): + with self.lock: + return self.data + + def SetData(self, data): + with self.lock: + self.data = data + + +class G1_29_ArmController: + def __init__(self, motion_mode=False, simulation_mode=False): + logger_mp.info("Initialize G1_29_ArmController...") + self.q_target = np.zeros(14) + self.tauff_target = np.zeros(14) + self.motion_mode = motion_mode + self.simulation_mode = simulation_mode + self.kp_high = 40.0 + self.kd_high = 3.0 + self.kp_low = 80.0 + self.kd_low = 3.0 + self.kp_wrist = 40.0 + self.kd_wrist = 1.5 + + self.all_motor_q = None + self.arm_velocity_limit = 100.0 + self.control_dt = 1.0 / 250.0 + + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None + + # initialize lowcmd publisher and lowstate subscriber + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + if self.motion_mode: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Motion, hg_LowCmd) + else: + self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Debug, hg_LowCmd) + self.lowcmd_publisher.Init() + self.lowstate_subscriber = ChannelSubscriber(kTopicLowState, hg_LowState) + self.lowstate_subscriber.Init() + self.lowstate_buffer = DataBuffer() + + # initialize subscribe thread + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() + + while not self.lowstate_buffer.GetData(): + time.sleep(0.1) + logger_mp.warning("[G1_29_ArmController] Waiting to subscribe dds...") + logger_mp.info("[G1_29_ArmController] Subscribe dds ok.") + + # initialize hg's lowcmd msg + self.crc = CRC() + self.msg = unitree_hg_msg_dds__LowCmd_() + self.msg.mode_pr = 0 + self.msg.mode_machine = self.get_mode_machine() + print(self.msg) + + self.all_motor_q = self.get_current_motor_q() + logger_mp.info(f"Current all body motor state q:\n{self.all_motor_q} \n") + logger_mp.info(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + logger_mp.info("Lock all joints except two arms...\n") + + arm_indices = set(member.value for member in G1_29_JointArmIndex) + for id in G1_29_JointIndex: + self.msg.motor_cmd[id].mode = 1 + if id.value in arm_indices: + if self._Is_wrist_motor(id): + self.msg.motor_cmd[id].kp = self.kp_wrist + self.msg.motor_cmd[id].kd = self.kd_wrist + else: + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].q = self.all_motor_q[id] + #print current motor q, kp, kd + + logger_mp.info("Lock OK!\n") #motors are not locked x + # for i in range(10000): + # print(self.get_current_motor_q()) + # time.sleep(0.05) + # initialize publish thread + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() + + logger_mp.info("Initialize G1_29_ArmController OK!\n") + + def _subscribe_motor_state(self): + while True: + msg = self.lowstate_subscriber.Read() + if msg is not None: + lowstate = G1_29_LowState() + for id in range(G1_29_Num_Motors): + lowstate.motor_state[id].q = msg.motor_state[id].q + lowstate.motor_state[id].dq = msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): + if self.motion_mode: + self.msg.motor_cmd[G1_29_JointIndex.kNotUsedJoint0].q = 1.0 + + while True: + start_time = time.time() + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + if self.simulation_mode: + cliped_arm_q_target = arm_q_target + else: + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit=self.arm_velocity_limit) + + for idx, id in enumerate(G1_29_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # logger_mp.debug(f"arm_velocity_limit:{self.arm_velocity_limit}") + # logger_mp.debug(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + """Set control target values q & tau of the left and right arm motors.""" + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target + + def get_mode_machine(self): + """Return current dds mode machine.""" + return self.lowstate_subscriber.Read().mode_machine + + def get_current_motor_q(self): + """Return current state q of all body motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointIndex]) + + def get_current_dual_arm_q(self): + """Return current state q of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointArmIndex]) + + def get_current_dual_arm_dq(self): + """Return current state dq of the left and right arm motors.""" + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in G1_29_JointArmIndex]) + + def ctrl_dual_arm_go_home(self): + """Move both the left and right arms of the robot to their home position by setting the target joint angles (q) and torques (tau) to zero.""" + logger_mp.info("[G1_29_ArmController] ctrl_dual_arm_go_home start...") + max_attempts = 100 + current_attempts = 0 + with self.ctrl_lock: + self.q_target = np.zeros(14) + #self.q_target[G1_29_JointIndex.kLeftElbow] = 0.5 + # self.tauff_target = np.zeros(14) + tolerance = 0.05 # Tolerance threshold for joint angles to determine "close to zero", can be adjusted based on your motor's precision requirements + while current_attempts < max_attempts: + current_q = self.get_current_dual_arm_q() + if np.all(np.abs(current_q) < tolerance): + if self.motion_mode: + for weight in np.linspace(1, 0, num=101): + self.msg.motor_cmd[G1_29_JointIndex.kNotUsedJoint0].q = weight + time.sleep(0.02) + logger_mp.info("[G1_29_ArmController] both arms have reached the home position.") + break + current_attempts += 1 + time.sleep(0.05) + + def speed_gradual_max(self, t=5.0): + """Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.""" + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True + + def speed_instant_max(self): + """set arms velocity to the maximum value immediately, instead of gradually increasing.""" + self.arm_velocity_limit = 30.0 + + def _Is_weak_motor(self, motor_index): + weak_motors = [ + G1_29_JointIndex.kLeftAnklePitch.value, + G1_29_JointIndex.kRightAnklePitch.value, + # Left arm + G1_29_JointIndex.kLeftShoulderPitch.value, + G1_29_JointIndex.kLeftShoulderRoll.value, + G1_29_JointIndex.kLeftShoulderYaw.value, + G1_29_JointIndex.kLeftElbow.value, + # Right arm + G1_29_JointIndex.kRightShoulderPitch.value, + G1_29_JointIndex.kRightShoulderRoll.value, + G1_29_JointIndex.kRightShoulderYaw.value, + G1_29_JointIndex.kRightElbow.value, + ] + return motor_index.value in weak_motors + + def _Is_wrist_motor(self, motor_index): + wrist_motors = [ + G1_29_JointIndex.kLeftWristRoll.value, + G1_29_JointIndex.kLeftWristPitch.value, + G1_29_JointIndex.kLeftWristyaw.value, + G1_29_JointIndex.kRightWristRoll.value, + G1_29_JointIndex.kRightWristPitch.value, + G1_29_JointIndex.kRightWristYaw.value, + ] + return motor_index.value in wrist_motors + + +class G1_29_JointArmIndex(IntEnum): + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + + +class G1_29_JointIndex(IntEnum): + # Left leg + kLeftHipPitch = 0 + kLeftHipRoll = 1 + kLeftHipYaw = 2 + kLeftKnee = 3 + kLeftAnklePitch = 4 + kLeftAnkleRoll = 5 + + # Right leg + kRightHipPitch = 6 + kRightHipRoll = 7 + kRightHipYaw = 8 + kRightKnee = 9 + kRightAnklePitch = 10 + kRightAnkleRoll = 11 + + kWaistYaw = 12 #we're c + kWaistRoll = 13 + kWaistPitch = 14 + + # Left arm + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 + + # Right arm + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + + # not used + kNotUsedJoint0 = 29 + kNotUsedJoint1 = 30 + kNotUsedJoint2 = 31 + kNotUsedJoint3 = 32 + kNotUsedJoint4 = 33 + kNotUsedJoint5 = 34 + diff --git a/eval_robot/robot_control/robot_hand_brainco.py b/eval_robot/robot_control/robot_hand_brainco.py new file mode 100644 index 000000000..f95d28b60 --- /dev/null +++ b/eval_robot/robot_control/robot_hand_brainco.py @@ -0,0 +1,196 @@ +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds +from unitree_sdk2py.idl.unitree_go.msg.dds_ import MotorCmds_, MotorStates_ # idl +from unitree_sdk2py.idl.default import unitree_go_msg_dds__MotorCmd_ + +import numpy as np +from enum import IntEnum +import threading +import time +from multiprocessing import Process, Array + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + +brainco_Num_Motors = 6 +kTopicbraincoLeftCommand = "rt/brainco/left/cmd" +kTopicbraincoLeftState = "rt/brainco/left/state" +kTopicbraincoRightCommand = "rt/brainco/right/cmd" +kTopicbraincoRightState = "rt/brainco/right/state" + + +class Brainco_Controller: + def __init__( + self, + left_hand_array, + right_hand_array, + dual_hand_data_lock=None, + dual_hand_state_array=None, + dual_hand_action_array=None, + fps=100.0, + Unit_Test=False, + simulation_mode=False, + ): + logger_mp.info("Initialize Brainco_Controller...") + self.fps = fps + self.hand_sub_ready = False + self.Unit_Test = Unit_Test + self.simulation_mode = simulation_mode + + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + # initialize handcmd publisher and handstate subscriber + self.LeftHandCmb_publisher = ChannelPublisher(kTopicbraincoLeftCommand, MotorCmds_) + self.LeftHandCmb_publisher.Init() + self.RightHandCmb_publisher = ChannelPublisher(kTopicbraincoRightCommand, MotorCmds_) + self.RightHandCmb_publisher.Init() + + self.LeftHandState_subscriber = ChannelSubscriber(kTopicbraincoLeftState, MotorStates_) + self.LeftHandState_subscriber.Init() + self.RightHandState_subscriber = ChannelSubscriber(kTopicbraincoRightState, MotorStates_) + self.RightHandState_subscriber.Init() + + # Shared Arrays for hand states + self.left_hand_state_array = Array("d", brainco_Num_Motors, lock=True) + self.right_hand_state_array = Array("d", brainco_Num_Motors, lock=True) + + # initialize subscribe thread + self.subscribe_state_thread = threading.Thread(target=self._subscribe_hand_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + while not self.hand_sub_ready: + time.sleep(0.1) + logger_mp.warning("[brainco_Controller] Waiting to subscribe dds...") + logger_mp.info("[brainco_Controller] Subscribe dds ok.") + + hand_control_process = Process( + target=self.control_process, + args=( + left_hand_array, + right_hand_array, + self.left_hand_state_array, + self.right_hand_state_array, + dual_hand_data_lock, + dual_hand_state_array, + dual_hand_action_array, + ), + ) + hand_control_process.daemon = True + hand_control_process.start() + + logger_mp.info("Initialize brainco_Controller OK!\n") + + def _subscribe_hand_state(self): + while True: + left_hand_msg = self.LeftHandState_subscriber.Read() + right_hand_msg = self.RightHandState_subscriber.Read() + self.hand_sub_ready = True + if left_hand_msg is not None and right_hand_msg is not None: + # Update left hand state + for idx, id in enumerate(Brainco_Left_Hand_JointIndex): + self.left_hand_state_array[idx] = left_hand_msg.states[id].q + # Update right hand state + for idx, id in enumerate(Brainco_Right_Hand_JointIndex): + self.right_hand_state_array[idx] = right_hand_msg.states[id].q + time.sleep(0.002) + + def ctrl_dual_hand(self, left_q_target, right_q_target): + """ + Set current left, right hand motor state target q + """ + for idx, id in enumerate(Brainco_Left_Hand_JointIndex): + self.left_hand_msg.cmds[id].q = left_q_target[idx] + for idx, id in enumerate(Brainco_Right_Hand_JointIndex): + self.right_hand_msg.cmds[id].q = right_q_target[idx] + + self.LeftHandCmb_publisher.Write(self.left_hand_msg) + self.RightHandCmb_publisher.Write(self.right_hand_msg) + # logger_mp.debug("hand ctrl publish ok.") + + def control_process( + self, + left_hand_array, + right_hand_array, + left_hand_state_array, + right_hand_state_array, + dual_hand_data_lock=None, + dual_hand_state_array=None, + dual_hand_action_array=None, + ): + self.running = True + + left_q_target = np.full(brainco_Num_Motors, 0) + right_q_target = np.full(brainco_Num_Motors, 0) + + # initialize brainco hand's cmd msg + self.left_hand_msg = MotorCmds_() + self.left_hand_msg.cmds = [unitree_go_msg_dds__MotorCmd_() for _ in range(len(Brainco_Left_Hand_JointIndex))] + self.right_hand_msg = MotorCmds_() + self.right_hand_msg.cmds = [unitree_go_msg_dds__MotorCmd_() for _ in range(len(Brainco_Right_Hand_JointIndex))] + + for idx, id in enumerate(Brainco_Left_Hand_JointIndex): + self.left_hand_msg.cmds[id].q = 0.0 + self.left_hand_msg.cmds[id].dq = 1.0 + for idx, id in enumerate(Brainco_Right_Hand_JointIndex): + self.right_hand_msg.cmds[id].q = 0.0 + self.right_hand_msg.cmds[id].dq = 1.0 + + try: + while self.running: + start_time = time.time() + # get dual hand state + with left_hand_array.get_lock(): + left_hand_mat = np.array(left_hand_array[:]).copy() + with right_hand_array.get_lock(): + right_hand_mat = np.array(right_hand_array[:]).copy() + + # Read left and right q_state from shared arrays + state_data = np.concatenate((np.array(left_hand_state_array[:]), np.array(right_hand_state_array[:]))) + + action_data = np.concatenate((left_hand_mat, right_hand_mat)) + if dual_hand_data_lock is not None: + with dual_hand_data_lock: + dual_hand_state_array[:] = state_data + dual_hand_action_array[:] = action_data + + if dual_hand_state_array and dual_hand_action_array: + with dual_hand_data_lock: + left_q_target = left_hand_mat + right_q_target = right_hand_mat + + self.ctrl_dual_hand(left_q_target, right_q_target) + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / self.fps) - time_elapsed) + time.sleep(sleep_time) + finally: + logger_mp.info("brainco_Controller has been closed.") + + +# according to the official documentation, https://www.brainco-hz.com/docs/revolimb-hand/product/parameters.html +# the motor sequence is as shown in the table below +# ┌──────┬───────┬────────────┬────────┬────────┬────────┬────────┐ +# │ Id │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ +# ├──────┼───────┼────────────┼────────┼────────┼────────┼────────┤ +# │Joint │ thumb │ thumb-aux | index │ middle │ ring │ pinky │ +# └──────┴───────┴────────────┴────────┴────────┴────────┴────────┘ +class Brainco_Right_Hand_JointIndex(IntEnum): + kRightHandThumb = 0 + kRightHandThumbAux = 1 + kRightHandIndex = 2 + kRightHandMiddle = 3 + kRightHandRing = 4 + kRightHandPinky = 5 + + +class Brainco_Left_Hand_JointIndex(IntEnum): + kLeftHandThumb = 0 + kLeftHandThumbAux = 1 + kLeftHandIndex = 2 + kLeftHandMiddle = 3 + kLeftHandRing = 4 + kLeftHandPinky = 5 diff --git a/eval_robot/robot_control/robot_hand_inspire.py b/eval_robot/robot_control/robot_hand_inspire.py new file mode 100644 index 000000000..dcc971140 --- /dev/null +++ b/eval_robot/robot_control/robot_hand_inspire.py @@ -0,0 +1,187 @@ +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds +from unitree_sdk2py.idl.unitree_go.msg.dds_ import MotorCmds_, MotorStates_ # idl +from unitree_sdk2py.idl.default import unitree_go_msg_dds__MotorCmd_ + +import numpy as np +from enum import IntEnum +import threading +import time +from multiprocessing import Process, Array + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + +Inspire_Num_Motors = 6 +kTopicInspireCommand = "rt/inspire/cmd" +kTopicInspireState = "rt/inspire/state" + + +class Inspire_Controller: + def __init__( + self, + left_hand_array, + right_hand_array, + dual_hand_data_lock=None, + dual_hand_state_array=None, + dual_hand_action_array=None, + fps=100.0, + Unit_Test=False, + simulation_mode=False, + ): + logger_mp.info("Initialize Inspire_Controller...") + self.fps = fps + self.Unit_Test = Unit_Test + self.simulation_mode = simulation_mode + + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + # initialize handcmd publisher and handstate subscriber + self.HandCmb_publisher = ChannelPublisher(kTopicInspireCommand, MotorCmds_) + self.HandCmb_publisher.Init() + + self.HandState_subscriber = ChannelSubscriber(kTopicInspireState, MotorStates_) + self.HandState_subscriber.Init() + + # Shared Arrays for hand states + self.left_hand_state_array = Array("d", Inspire_Num_Motors, lock=True) + self.right_hand_state_array = Array("d", Inspire_Num_Motors, lock=True) + + # initialize subscribe thread + self.subscribe_state_thread = threading.Thread(target=self._subscribe_hand_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + while True: + if any(self.right_hand_state_array): # any(self.left_hand_state_array) and + break + time.sleep(0.01) + logger_mp.warning("[Inspire_Controller] Waiting to subscribe dds...") + logger_mp.info("[Inspire_Controller] Subscribe dds ok.") + + hand_control_process = Process( + target=self.control_process, + args=( + left_hand_array, + right_hand_array, + self.left_hand_state_array, + self.right_hand_state_array, + dual_hand_data_lock, + dual_hand_state_array, + dual_hand_action_array, + ), + ) + hand_control_process.daemon = True + hand_control_process.start() + + logger_mp.info("Initialize Inspire_Controller OK!\n") + + def _subscribe_hand_state(self): + while True: + hand_msg = self.HandState_subscriber.Read() + if hand_msg is not None: + for idx, id in enumerate(Inspire_Left_Hand_JointIndex): + self.left_hand_state_array[idx] = hand_msg.states[id].q + for idx, id in enumerate(Inspire_Right_Hand_JointIndex): + self.right_hand_state_array[idx] = hand_msg.states[id].q + time.sleep(0.002) + + def ctrl_dual_hand(self, left_q_target, right_q_target): + """ + Set current left, right hand motor state target q + """ + for idx, id in enumerate(Inspire_Left_Hand_JointIndex): + self.hand_msg.cmds[id].q = left_q_target[idx] + for idx, id in enumerate(Inspire_Right_Hand_JointIndex): + self.hand_msg.cmds[id].q = right_q_target[idx] + + self.HandCmb_publisher.Write(self.hand_msg) + # logger_mp.debug("hand ctrl publish ok.") + + def control_process( + self, + left_hand_array, + right_hand_array, + left_hand_state_array, + right_hand_state_array, + dual_hand_data_lock=None, + dual_hand_state_array=None, + dual_hand_action_array=None, + ): + self.running = True + + left_q_target = np.full(Inspire_Num_Motors, 1.0) + right_q_target = np.full(Inspire_Num_Motors, 1.0) + + # initialize inspire hand's cmd msg + self.hand_msg = MotorCmds_() + self.hand_msg.cmds = [ + unitree_go_msg_dds__MotorCmd_() + for _ in range(len(Inspire_Right_Hand_JointIndex) + len(Inspire_Left_Hand_JointIndex)) + ] + + for idx, id in enumerate(Inspire_Left_Hand_JointIndex): + self.hand_msg.cmds[id].q = 1.0 + for idx, id in enumerate(Inspire_Right_Hand_JointIndex): + self.hand_msg.cmds[id].q = 1.0 + + try: + while self.running: + start_time = time.time() + + # get dual hand state + with left_hand_array.get_lock(): + left_hand_mat = np.array(left_hand_array[:]).copy() + with right_hand_array.get_lock(): + right_hand_mat = np.array(right_hand_array[:]).copy() + + # Read left and right q_state from shared arrays + state_data = np.concatenate((np.array(left_hand_state_array[:]), np.array(right_hand_state_array[:]))) + + action_data = np.concatenate((left_hand_mat, right_hand_mat)) + if dual_hand_data_lock is not None: + with dual_hand_data_lock: + dual_hand_state_array[:] = state_data + dual_hand_action_array[:] = action_data + + if dual_hand_state_array and dual_hand_action_array: + with dual_hand_data_lock: + left_q_target = left_hand_mat + right_q_target = right_hand_mat + + self.ctrl_dual_hand(left_q_target, right_q_target) + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / self.fps) - time_elapsed) + time.sleep(sleep_time) + finally: + logger_mp.info("Inspire_Controller has been closed.") + + +# Update hand state, according to the official documentation, https://support.unitree.com/home/en/G1_developer/inspire_dfx_dexterous_hand +# the state sequence is as shown in the table below +# ┌──────┬───────┬──────┬────────┬────────┬────────────┬────────────────┬───────┬──────┬────────┬────────┬────────────┬────────────────┐ +# │ Id │ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 │ 8 │ 9 │ 10 │ 11 │ +# ├──────┼───────┼──────┼────────┼────────┼────────────┼────────────────┼───────┼──────┼────────┼────────┼────────────┼────────────────┤ +# │ │ Right Hand │ Left Hand │ +# │Joint │ pinky │ ring │ middle │ index │ thumb-bend │ thumb-rotation │ pinky │ ring │ middle │ index │ thumb-bend │ thumb-rotation │ +# └──────┴───────┴──────┴────────┴────────┴────────────┴────────────────┴───────┴──────┴────────┴────────┴────────────┴────────────────┘ +class Inspire_Right_Hand_JointIndex(IntEnum): + kRightHandPinky = 0 + kRightHandRing = 1 + kRightHandMiddle = 2 + kRightHandIndex = 3 + kRightHandThumbBend = 4 + kRightHandThumbRotation = 5 + + +class Inspire_Left_Hand_JointIndex(IntEnum): + kLeftHandPinky = 6 + kLeftHandRing = 7 + kLeftHandMiddle = 8 + kLeftHandIndex = 9 + kLeftHandThumbBend = 10 + kLeftHandThumbRotation = 11 diff --git a/eval_robot/robot_control/robot_hand_unitree.py b/eval_robot/robot_control/robot_hand_unitree.py new file mode 100644 index 000000000..013c43c34 --- /dev/null +++ b/eval_robot/robot_control/robot_hand_unitree.py @@ -0,0 +1,403 @@ +# for dex3-1 +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import HandCmd_, HandState_ # idl +from unitree_sdk2py.idl.default import unitree_hg_msg_dds__HandCmd_ + +# for gripper +from unitree_sdk2py.idl.unitree_go.msg.dds_ import MotorCmds_, MotorStates_ # idl +from unitree_sdk2py.idl.default import unitree_go_msg_dds__MotorCmd_ + +import numpy as np +from enum import IntEnum +import time +import threading +from multiprocessing import Process, Value, Array + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +unitree_tip_indices = [4, 9, 14] # [thumb, index, middle] in OpenXR +Dex3_Num_Motors = 7 +kTopicDex3LeftCommand = "rt/dex3/left/cmd" +kTopicDex3RightCommand = "rt/dex3/right/cmd" +kTopicDex3LeftState = "rt/dex3/left/state" +kTopicDex3RightState = "rt/dex3/right/state" + + +class Dex3_1_Controller: + def __init__( + self, + left_hand_array_in, + right_hand_array_in, + dual_hand_data_lock=None, + dual_hand_state_array_out=None, + dual_hand_action_array_out=None, + fps=100.0, + Unit_Test=False, + simulation_mode=False, + ): + """ + [note] A *_array type parameter requires using a multiprocessing Array, because it needs to be passed to the internal child process + left_hand_array_in: [input] Left hand skeleton data (required from XR device) to hand_ctrl.control_process + right_hand_array_in: [input] Right hand skeleton data (required from XR device) to hand_ctrl.control_process + dual_hand_data_lock: Data synchronization lock for dual_hand_state_array and dual_hand_action_array + dual_hand_state_array_out: [output] Return left(7), right(7) hand motor state + dual_hand_action_array_out: [output] Return left(7), right(7) hand motor action + fps: Control frequency + Unit_Test: Whether to enable unit testing + simulation_mode: Whether to use simulation mode (default is False, which means using real robot) + """ + logger_mp.info("Initialize Dex3_1_Controller...") + + self.fps = fps + self.Unit_Test = Unit_Test + self.simulation_mode = simulation_mode + + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + # initialize handcmd publisher and handstate subscriber + self.LeftHandCmb_publisher = ChannelPublisher(kTopicDex3LeftCommand, HandCmd_) + self.LeftHandCmb_publisher.Init() + self.RightHandCmb_publisher = ChannelPublisher(kTopicDex3RightCommand, HandCmd_) + self.RightHandCmb_publisher.Init() + + self.LeftHandState_subscriber = ChannelSubscriber(kTopicDex3LeftState, HandState_) + self.LeftHandState_subscriber.Init() + self.RightHandState_subscriber = ChannelSubscriber(kTopicDex3RightState, HandState_) + self.RightHandState_subscriber.Init() + + # Shared Arrays for hand states + self.left_hand_state_array = Array("d", Dex3_Num_Motors, lock=True) + self.right_hand_state_array = Array("d", Dex3_Num_Motors, lock=True) + + # initialize subscribe thread + self.subscribe_state_thread = threading.Thread(target=self._subscribe_hand_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + while True: + if any(self.left_hand_state_array) and any(self.right_hand_state_array): + break + time.sleep(0.01) + logger_mp.warning("[Dex3_1_Controller] Waiting to subscribe dds...") + logger_mp.info("[Dex3_1_Controller] Subscribe dds ok.") + + hand_control_process = Process( + target=self.control_process, + args=( + left_hand_array_in, + right_hand_array_in, + self.left_hand_state_array, + self.right_hand_state_array, + dual_hand_data_lock, + dual_hand_state_array_out, + dual_hand_action_array_out, + ), + ) + hand_control_process.daemon = True + hand_control_process.start() + + logger_mp.info("Initialize Dex3_1_Controller OK!\n") + + def _subscribe_hand_state(self): + while True: + left_hand_msg = self.LeftHandState_subscriber.Read() + right_hand_msg = self.RightHandState_subscriber.Read() + if left_hand_msg is not None and right_hand_msg is not None: + # Update left hand state + for idx, id in enumerate(Dex3_1_Left_JointIndex): + self.left_hand_state_array[idx] = left_hand_msg.motor_state[id].q + # Update right hand state + for idx, id in enumerate(Dex3_1_Right_JointIndex): + self.right_hand_state_array[idx] = right_hand_msg.motor_state[id].q + time.sleep(0.002) + + class _RIS_Mode: + def __init__(self, id=0, status=0x01, timeout=0): + self.motor_mode = 0 + self.id = id & 0x0F # 4 bits for id + self.status = status & 0x07 # 3 bits for status + self.timeout = timeout & 0x01 # 1 bit for timeout + + def _mode_to_uint8(self): + self.motor_mode |= self.id & 0x0F + self.motor_mode |= (self.status & 0x07) << 4 + self.motor_mode |= (self.timeout & 0x01) << 7 + return self.motor_mode + + def ctrl_dual_hand(self, left_q_target, right_q_target): + """set current left, right hand motor state target q""" + for idx, id in enumerate(Dex3_1_Left_JointIndex): + self.left_msg.motor_cmd[id].q = left_q_target[idx] + for idx, id in enumerate(Dex3_1_Right_JointIndex): + self.right_msg.motor_cmd[id].q = right_q_target[idx] + + self.LeftHandCmb_publisher.Write(self.left_msg) + self.RightHandCmb_publisher.Write(self.right_msg) + # logger_mp.debug("hand ctrl publish ok.") + + def control_process( + self, + left_hand_array_in, + right_hand_array_in, + left_hand_state_array, + right_hand_state_array, + dual_hand_data_lock=None, + dual_hand_state_array_out=None, + dual_hand_action_array_out=None, + ): + self.running = True + + # left_q_target = np.full(Dex3_Num_Motors, 0) + # right_q_target = np.full(Dex3_Num_Motors, 0) + + q = 0.0 + dq = 0.0 + tau = 0.0 + kp = 1.5 + kd = 0.2 + + # initialize dex3-1's left hand cmd msg + self.left_msg = unitree_hg_msg_dds__HandCmd_() + for id in Dex3_1_Left_JointIndex: + ris_mode = self._RIS_Mode(id=id, status=0x01) + motor_mode = ris_mode._mode_to_uint8() + self.left_msg.motor_cmd[id].mode = motor_mode + self.left_msg.motor_cmd[id].q = q + self.left_msg.motor_cmd[id].dq = dq + self.left_msg.motor_cmd[id].tau = tau + self.left_msg.motor_cmd[id].kp = kp + self.left_msg.motor_cmd[id].kd = kd + + # initialize dex3-1's right hand cmd msg + self.right_msg = unitree_hg_msg_dds__HandCmd_() + for id in Dex3_1_Right_JointIndex: + ris_mode = self._RIS_Mode(id=id, status=0x01) + motor_mode = ris_mode._mode_to_uint8() + self.right_msg.motor_cmd[id].mode = motor_mode + self.right_msg.motor_cmd[id].q = q + self.right_msg.motor_cmd[id].dq = dq + self.right_msg.motor_cmd[id].tau = tau + self.right_msg.motor_cmd[id].kp = kp + self.right_msg.motor_cmd[id].kd = kd + + try: + while self.running: + start_time = time.time() + + # get dual hand state + with left_hand_array_in.get_lock(): + left_hand_mat = np.array(left_hand_array_in[:]).copy() + with right_hand_array_in.get_lock(): + right_hand_mat = np.array(right_hand_array_in[:]).copy() + + # Read left and right q_state from shared arrays + state_data = np.concatenate((np.array(left_hand_state_array[:]), np.array(right_hand_state_array[:]))) + + # get dual hand action + action_data = np.concatenate((left_hand_mat, right_hand_mat)) + if dual_hand_state_array_out and dual_hand_action_array_out: + with dual_hand_data_lock: + dual_hand_state_array_out[:] = state_data + dual_hand_action_array_out[:] = action_data + + self.ctrl_dual_hand(left_hand_mat, right_hand_mat) + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / self.fps) - time_elapsed) + time.sleep(sleep_time) + finally: + print("Dex3_1_Controller has been closed.") + + +class Dex3_1_Left_JointIndex(IntEnum): + kLeftHandThumb0 = 0 + kLeftHandThumb1 = 1 + kLeftHandThumb2 = 2 + kLeftHandMiddle0 = 3 + kLeftHandMiddle1 = 4 + kLeftHandIndex0 = 5 + kLeftHandIndex1 = 6 + + +class Dex3_1_Right_JointIndex(IntEnum): + kRightHandThumb0 = 0 + kRightHandThumb1 = 1 + kRightHandThumb2 = 2 + kRightHandIndex0 = 3 + kRightHandIndex1 = 4 + kRightHandMiddle0 = 5 + kRightHandMiddle1 = 6 + + +kTopicGripperLeftCommand = "rt/dex1/left/cmd" +kTopicGripperLeftState = "rt/dex1/left/state" +kTopicGripperRightCommand = "rt/dex1/right/cmd" +kTopicGripperRightState = "rt/dex1/right/state" + + +class Dex1_1_Gripper_Controller: + def __init__( + self, + left_gripper_value_in, + right_gripper_value_in, + dual_gripper_data_lock=None, + dual_gripper_state_out=None, + dual_gripper_action_out=None, + filter=True, + fps=200.0, + Unit_Test=False, + simulation_mode=False, + ): + """ + [note] A *_array type parameter requires using a multiprocessing Array, because it needs to be passed to the internal child process + left_gripper_value_in: [input] Left ctrl data (required from XR device) to control_thread + right_gripper_value_in: [input] Right ctrl data (required from XR device) to control_thread + dual_gripper_data_lock: Data synchronization lock for dual_gripper_state_array and dual_gripper_action_array + dual_gripper_state_out: [output] Return left(1), right(1) gripper motor state + dual_gripper_action_out: [output] Return left(1), right(1) gripper motor action + fps: Control frequency + Unit_Test: Whether to enable unit testing + simulation_mode: Whether to use simulation mode (default is False, which means using real robot) + """ + + logger_mp.info("Initialize Dex1_1_Gripper_Controller...") + + self.fps = fps + self.Unit_Test = Unit_Test + self.gripper_sub_ready = False + self.simulation_mode = simulation_mode + + if self.simulation_mode: + ChannelFactoryInitialize(1) + else: + ChannelFactoryInitialize(0) + + # initialize handcmd publisher and handstate subscriber + self.LeftGripperCmb_publisher = ChannelPublisher(kTopicGripperLeftCommand, MotorCmds_) + self.LeftGripperCmb_publisher.Init() + self.RightGripperCmb_publisher = ChannelPublisher(kTopicGripperRightCommand, MotorCmds_) + self.RightGripperCmb_publisher.Init() + + self.LeftGripperState_subscriber = ChannelSubscriber(kTopicGripperLeftState, MotorStates_) + self.LeftGripperState_subscriber.Init() + self.RightGripperState_subscriber = ChannelSubscriber(kTopicGripperRightState, MotorStates_) + self.RightGripperState_subscriber.Init() + + # Shared Arrays for gripper states + self.left_gripper_state_value = Value("d", 0.0, lock=True) + self.right_gripper_state_value = Value("d", 0.0, lock=True) + + # initialize subscribe thread + self.subscribe_state_thread = threading.Thread(target=self._subscribe_gripper_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + while not self.gripper_sub_ready: + time.sleep(0.01) + logger_mp.warning("[Dex1_1_Gripper_Controller] Waiting to subscribe dds...") + logger_mp.info("[Dex1_1_Gripper_Controller] Subscribe dds ok.") + + self.gripper_control_thread = threading.Thread( + target=self.control_thread, + args=( + left_gripper_value_in, + right_gripper_value_in, + self.left_gripper_state_value, + self.right_gripper_state_value, + dual_gripper_data_lock, + dual_gripper_state_out, + dual_gripper_action_out, + ), + ) + self.gripper_control_thread.daemon = True + self.gripper_control_thread.start() + + logger_mp.info("Initialize Dex1_1_Gripper_Controller OK!\n") + + def _subscribe_gripper_state(self): + while True: + left_gripper_msg = self.LeftGripperState_subscriber.Read() + right_gripper_msg = self.RightGripperState_subscriber.Read() + self.gripper_sub_ready = True + if left_gripper_msg is not None and right_gripper_msg is not None: + self.left_gripper_state_value.value = left_gripper_msg.states[0].q + self.right_gripper_state_value.value = right_gripper_msg.states[0].q + time.sleep(0.002) + + def ctrl_dual_gripper(self, dual_gripper_action): + """set current left, right gripper motor cmd target q""" + self.left_gripper_msg.cmds[0].q = dual_gripper_action[0] + self.right_gripper_msg.cmds[0].q = dual_gripper_action[1] + + self.LeftGripperCmb_publisher.Write(self.left_gripper_msg) + self.RightGripperCmb_publisher.Write(self.right_gripper_msg) + # logger_mp.debug("gripper ctrl publish ok.") + + def control_thread( + self, + left_gripper_value_in, + right_gripper_value_in, + left_gripper_state_value, + right_gripper_state_value, + dual_hand_data_lock=None, + dual_gripper_state_out=None, + dual_gripper_action_out=None, + ): + self.running = True + LEFT_MAPPED_MIN = 0.0 # The minimum initial motor position when the gripper closes at startup. + RIGHT_MAPPED_MIN = 0.0 # The minimum initial motor position when the gripper closes at startup. + # The maximum initial motor position when the gripper closes before calibration (with the rail stroke calculated as 0.6 cm/rad * 9 rad = 5.4 cm). + + dq = 0.0 + tau = 0.0 + kp = 5.00 + kd = 0.05 + # initialize gripper cmd msg + self.left_gripper_msg = MotorCmds_() + self.left_gripper_msg.cmds = [unitree_go_msg_dds__MotorCmd_()] + self.right_gripper_msg = MotorCmds_() + self.right_gripper_msg.cmds = [unitree_go_msg_dds__MotorCmd_()] + + self.left_gripper_msg.cmds[0].dq = dq + self.left_gripper_msg.cmds[0].tau = tau + self.left_gripper_msg.cmds[0].kp = kp + self.left_gripper_msg.cmds[0].kd = kd + + self.right_gripper_msg.cmds[0].dq = dq + self.right_gripper_msg.cmds[0].tau = tau + self.right_gripper_msg.cmds[0].kp = kp + self.right_gripper_msg.cmds[0].kd = kd + try: + while self.running: + start_time = time.time() + # get dual hand skeletal point state from XR device + with left_gripper_value_in.get_lock(): + left_gripper_value = left_gripper_value_in.value + with right_gripper_value_in.get_lock(): + right_gripper_value = right_gripper_value_in.value + # get current dual gripper motor state + dual_gripper_state = np.array([left_gripper_state_value.value, right_gripper_state_value.value]) + dual_gripper_action = np.array([left_gripper_value, right_gripper_value]) + + if dual_gripper_state_out and dual_gripper_action_out: + with dual_hand_data_lock: + dual_gripper_state_out[:] = dual_gripper_state - np.array([LEFT_MAPPED_MIN, RIGHT_MAPPED_MIN]) + dual_gripper_action_out[:] = dual_gripper_action - np.array([LEFT_MAPPED_MIN, RIGHT_MAPPED_MIN]) + self.ctrl_dual_gripper(dual_gripper_action) + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / self.fps) - time_elapsed) + time.sleep(sleep_time) + finally: + logger_mp.info("Dex1_1_Gripper_Controller has been closed.") + + +class Gripper_JointIndex(IntEnum): + kGripper = 0 diff --git a/eval_robot/teleop_sim.py b/eval_robot/teleop_sim.py new file mode 100644 index 000000000..f68889365 --- /dev/null +++ b/eval_robot/teleop_sim.py @@ -0,0 +1,98 @@ +# unitree_lerobot/eval_robot/teleop_sim_min.py +import time +import traceback +import numpy as np + +from src.lerobot.teleoperators.homunculus import ( + HomunculusArm, HomunculusArmConfig +) +from src.lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_test import G1_29_ArmController +from src.lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK + +# ---- map teleop (5 chans in [-1,1]) -> joint targets (rad) +# Order: S_pitch, S_yaw, S_roll, Elbow_flex, Wrist_roll +def scale_to_joint_limits(u5: np.ndarray) -> np.ndarray: + # Tweak these if your sim build expects the alternate set + mins = np.array([-3.05, 0.00, -2.30, -1.00, 1.95], dtype=np.float32) + maxs = np.array([ 2.65, 2.20, 2.30, 2.00, -1.00], dtype=np.float32) + u = np.clip(u5.astype(np.float32), -1.0, 1.0) + return mins + (u + 1.0) * 0.5 * (maxs - mins) + +def main(): + # --- Teleop (Homunculus) --- + EXO_PORT = "/dev/ttyACM0" # change if needed + EXO_ID = "unitree_left" + + exo_cfg = HomunculusArmConfig(EXO_PORT, id=EXO_ID) + exo = HomunculusArm(exo_cfg) + exo.connect(calibrate=True) + + # --- Robot (Simulation) --- + # No EvalRealConfig, no setup_robot_interface — direct construction + arm_ik = G1_29_ArmIK() + arm_ctrl = G1_29_ArmController(motion_mode=False, simulation_mode=True) + + # Determine DoF safely (fallback to 14 if not exposed) + arm_dof = getattr(arm_ctrl, "arm_dof", 14) + + # Optional: neutral pose + neutral = np.zeros(arm_dof, dtype=np.float32) + try: + tau0 = arm_ik.solve_tau(neutral) + arm_ctrl.ctrl_dual_arm(neutral, tau0) + except Exception: + pass + + # Control loop + HZ = 100.0 + DT = 1.0 / HZ + + # Simple smoothing to avoid jitter + q_prev = neutral.copy() + alpha = 0.35 # 0..1, higher = snappier + + try: + while True: + t0 = time.perf_counter() + + # 1) Read teleop dict (values ~ [-100, 100]); take first 5 channels + teleop = exo.get_action() # Ordered dict + vals = list(teleop.values())[:5] + + # Keep first, negate others (matches your earlier convention) + vals = [vals[0]] + [-v for v in vals[1:]] + + # Normalize to [-1, 1] + norm = np.asarray(vals, dtype=np.float32) / 100.0 + + # 2) Map to joint targets and assemble full arm command + q5 = scale_to_joint_limits(norm) + arm_cmd = np.zeros(arm_dof, dtype=np.float32) + arm_cmd[:5] = q5 + + # 3) Low-pass filter for smoothness + q_smooth = alpha * arm_cmd + (1.0 - alpha) * q_prev + q_prev = q_smooth + + # 4) Compute torques and send + tau = arm_ik.solve_tau(q_smooth) + arm_ctrl.ctrl_dual_arm(q_smooth, tau) + + # 5) rate control + elapsed = time.perf_counter() - t0 + if elapsed < DT: + time.sleep(DT - elapsed) + + except KeyboardInterrupt: + pass + except Exception: + traceback.print_exc() + finally: + try: + exo.disconnect() + except Exception: + pass + # Optionally: arm_ctrl.ctrl_dual_arm_go_home() + +if __name__ == "__main__": + main() diff --git a/eval_robot/test.py b/eval_robot/test.py new file mode 100644 index 000000000..b06f1f17a --- /dev/null +++ b/eval_robot/test.py @@ -0,0 +1,40 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import numpy as np +import cv2 +from multiprocessing.sharedctypes import SynchronizedArray +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from eval_robot.utils.utils import cleanup_resources, EvalRealConfig + +from eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data +from eval_robot.utils.utils import to_list, to_scalar +from eval_robot.robot_control.robot_arm_test import ( + G1_29_ArmController, G1_29_JointIndex +) +import logging_mp +from eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def replay_main(): + + #damp needs to be on? do i start the robot as well + + arm_ik = G1_29_ArmIK() + arm_ctrl = G1_29_ArmController(motion_mode=False, simulation_mode=False)#motors move here upon init. there's a bug where when closing python the motors die + +if __name__ == "__main__": + replay_main() diff --git a/eval_robot/utils/episode_writer.py b/eval_robot/utils/episode_writer.py new file mode 100644 index 000000000..d31d7d679 --- /dev/null +++ b/eval_robot/utils/episode_writer.py @@ -0,0 +1,219 @@ +import os +import cv2 +import json +import datetime +import numpy as np +import time + +from queue import Queue, Empty +from threading import Thread +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + + +class EpisodeWriter: + def __init__(self, task_dir, frequency=30, image_size=[640, 480]): + """ + image_size: [width, height] + """ + logger_mp.info("==> EpisodeWriter initializing...\n") + self.task_dir = task_dir + self.frequency = frequency + self.image_size = image_size + + self.data = {} + self.episode_data = [] + self.item_id = -1 + self.episode_id = -1 + if os.path.exists(self.task_dir): + episode_dirs = [episode_dir for episode_dir in os.listdir(self.task_dir) if "episode_" in episode_dir] + episode_last = sorted(episode_dirs)[-1] if len(episode_dirs) > 0 else None + self.episode_id = 0 if episode_last is None else int(episode_last.split("_")[-1]) + logger_mp.info(f"==> task_dir directory already exist, now self.episode_id is:{self.episode_id}\n") + else: + os.makedirs(self.task_dir) + logger_mp.info("==> episode directory does not exist, now create one.\n") + self.data_info() + self.text_desc() + self.result = None + self.is_available = True # Indicates whether the class is available for new operations + # Initialize the queue and worker thread + self.item_data_queue = Queue(-1) + self.stop_worker = False + self.need_save = False # Flag to indicate when save_episode is triggered + self.worker_thread = Thread(target=self.process_queue) + self.worker_thread.start() + + logger_mp.info("==> EpisodeWriter initialized successfully.\n") + + def data_info(self, version="1.0.0", date=None, author=None): + self.info = { + "version": "1.0.0" if version is None else version, + "date": datetime.date.today().strftime("%Y-%m-%d") if date is None else date, + "author": "unitree" if author is None else author, + "image": {"width": self.image_size[0], "height": self.image_size[1], "fps": self.frequency}, + "depth": {"width": self.image_size[0], "height": self.image_size[1], "fps": self.frequency}, + "audio": {"sample_rate": 16000, "channels": 1, "format": "PCM", "bits": 16}, # PCM_S16 + "joint_names": { + "left_arm": [ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristyaw", + ], + "left_ee": [], + "right_arm": [], + "right_ee": [], + "body": [], + }, + "tactile_names": { + "left_ee": [], + "right_ee": [], + }, + "sim_state": "", + } + + def text_desc(self): + self.text = { + "goal": "Place the wooden blocks into the yellow frame, stacking them from bottom to top in the order: red, yellow, green.", + "desc": "Using the gripper, first place the red wooden block into the yellow frame. Next, stack the yellow wooden block on top of the red one, and finally place the green wooden block on top of the yellow block.", + "steps": "", + } + + def create_episode(self): + """ + Create a new episode. + Returns: + bool: True if the episode is successfully created, False otherwise. + Note: + Once successfully created, this function will only be available again after save_episode complete its save task. + """ + if not self.is_available: + logger_mp.info( + "==> The class is currently unavailable for new operations. Please wait until ongoing tasks are completed." + ) + return False # Return False if the class is unavailable + + # Reset episode-related data and create necessary directories + self.item_id = -1 + self.episode_data = [] + self.episode_id = self.episode_id + 1 + + self.episode_dir = os.path.join(self.task_dir, f"episode_{str(self.episode_id).zfill(4)}") + self.color_dir = os.path.join(self.episode_dir, "colors") + self.depth_dir = os.path.join(self.episode_dir, "depths") + self.audio_dir = os.path.join(self.episode_dir, "audios") + self.json_path = os.path.join(self.episode_dir, "data.json") + os.makedirs(self.episode_dir, exist_ok=True) + os.makedirs(self.color_dir, exist_ok=True) + os.makedirs(self.depth_dir, exist_ok=True) + os.makedirs(self.audio_dir, exist_ok=True) + + self.is_available = False # After the episode is created, the class is marked as unavailable until the episode is successfully saved + logger_mp.info(f"==> New episode created: {self.episode_dir}") + return True # Return True if the episode is successfully created + + def add_item(self, colors, depths=None, states=None, actions=None, tactiles=None, audios=None, sim_state=None): + # Increment the item ID + self.item_id += 1 + # Create the item data dictionary + item_data = { + "idx": self.item_id, + "colors": colors, + "depths": depths, + "states": states, + "actions": actions, + "tactiles": tactiles, + "audios": audios, + "sim_state": sim_state, + } + # Enqueue the item data + self.item_data_queue.put(item_data) + + def process_queue(self): + while not self.stop_worker or not self.item_data_queue.empty(): + # Process items in the queue + try: + item_data = self.item_data_queue.get(timeout=1) + try: + self._process_item_data(item_data) + except Exception as e: + logger_mp.info(f"Error processing item_data (idx={item_data['idx']}): {e}") + self.item_data_queue.task_done() + except Empty: + pass + + # Check if save_episode was triggered + if self.need_save and self.item_data_queue.empty(): + self._save_episode() + + def _process_item_data(self, item_data): + idx = item_data["idx"] + colors = item_data.get("colors", {}) + depths = item_data.get("depths", {}) + audios = item_data.get("audios", {}) + + # Save images + if colors: + for idx_color, (color_key, color) in enumerate(colors.items()): + color_name = f"{str(idx).zfill(6)}_{color_key}.jpg" + if not cv2.imwrite(os.path.join(self.color_dir, color_name), color): + logger_mp.info("Failed to save color image.") + item_data["colors"][color_key] = os.path.join("colors", color_name) + + # Save depths + if depths: + for idx_depth, (depth_key, depth) in enumerate(depths.items()): + depth_name = f"{str(idx).zfill(6)}_{depth_key}.jpg" + if not cv2.imwrite(os.path.join(self.depth_dir, depth_name), depth): + logger_mp.info("Failed to save depth image.") + item_data["depths"][depth_key] = os.path.join("depths", depth_name) + + # Save audios + if audios: + for mic, audio in audios.items(): + audio_name = f"audio_{str(idx).zfill(6)}_{mic}.npy" + np.save(os.path.join(self.audio_dir, audio_name), audio.astype(np.int16)) + item_data["audios"][mic] = os.path.join("audios", audio_name) + + # Update episode data + self.episode_data.append(item_data) + + def save_episode(self, result): + """ + Trigger the save operation. This sets the save flag, and the process_queue thread will handle it. + """ + self.need_save = True # Set the save flag + self.result = result + logger_mp.info("==> Episode saved start...") + + def _save_episode(self): + """ + Save the episode data to a JSON file. + """ + self.data["info"] = self.info + self.data["text"] = self.text + self.data["data"] = self.episode_data + self.data["result"] = self.result + + with open(self.json_path, "w", encoding="utf-8") as jsonf: + jsonf.write(json.dumps(self.data, indent=4, ensure_ascii=False)) + self.need_save = False # Reset the save flag + self.is_available = True # Mark the class as available after saving + logger_mp.info(f"==> Episode saved successfully to {self.json_path} with result: {self.result}") + + def close(self): + """ + Stop the worker thread and ensure all tasks are completed. + """ + self.item_data_queue.join() + if not self.is_available: # If self.is_available is False, it means there is still data not saved. + self.save_episode(self.result) + while not self.is_available: + time.sleep(0.01) + self.stop_worker = True + self.worker_thread.join() diff --git a/eval_robot/utils/rerun_visualizer.py b/eval_robot/utils/rerun_visualizer.py new file mode 100644 index 000000000..0e2dc1277 --- /dev/null +++ b/eval_robot/utils/rerun_visualizer.py @@ -0,0 +1,166 @@ +import torch +from datetime import datetime +from typing import Any + +import rerun as rr +import rerun.blueprint as rrb + + +class RerunLogger: + """ + A fully automatic Rerun logger designed to parse and visualize step + dictionaries directly from a LeRobotDataset. + """ + + def __init__( + self, + prefix: str = "", + memory_limit: str = "200MB", + idxrangeboundary: int | None = 300, + ): + """Initializes the Rerun logger.""" + # Use a descriptive name for the Rerun recording + rr.init(f"Dataset_Log_{datetime.now().strftime('%Y%m%d_%H%M%S')}") + rr.spawn(memory_limit=memory_limit) + + self.prefix = prefix + self.blueprint_sent = False + self.idxrangeboundary = idxrangeboundary + + # --- Internal cache for discovered keys --- + self._image_keys: tuple[str, ...] = () + self._state_key: str = "" + self._action_key: str = "" + self._index_key: str = "index" + self._task_key: str = "task" + self._episode_index_key: str = "episode_index" + + self.current_episode = -1 + + def _initialize_from_data(self, step_data: dict[str, Any]): + """Inspects the first data dictionary to discover components and set up the blueprint.""" + print("RerunLogger: First data packet received. Auto-configuring...") + + image_keys = [] + for key, value in step_data.items(): + if key.startswith("observation.images.") and isinstance(value, torch.Tensor) and value.ndim > 2: + image_keys.append(key) + elif key == "observation.state": + self._state_key = key + elif key == "action": + self._action_key = key + + self._image_keys = tuple(sorted(image_keys)) + + if "index" in step_data: + self._index_key = "index" + elif "frame_index" in step_data: + self._index_key = "frame_index" + + print(f" - Using '{self._index_key}' for time sequence.") + print(f" - Detected State Key: '{self._state_key}'") + print(f" - Detected Action Key: '{self._action_key}'") + print(f" - Detected Image Keys: {self._image_keys}") + if self.idxrangeboundary: + self.setup_blueprint() + + def setup_blueprint(self): + """Sets up and sends the Rerun blueprint based on detected components.""" + views = [] + + for key in self._image_keys: + clean_name = key.replace("observation.images.", "") + entity_path = f"{self.prefix}images/{clean_name}" + views.append(rrb.Spatial2DView(origin=entity_path, name=clean_name)) + + if self._state_key: + entity_path = f"{self.prefix}state" + views.append( + rrb.TimeSeriesView( + origin=entity_path, + name="Observation State", + time_ranges=[ + rrb.VisibleTimeRange( + "frame", + start=rrb.TimeRangeBoundary.cursor_relative(seq=-self.idxrangeboundary), + end=rrb.TimeRangeBoundary.cursor_relative(), + ) + ], + plot_legend=rrb.PlotLegend(visible=True), + ) + ) + + if self._action_key: + entity_path = f"{self.prefix}action" + views.append( + rrb.TimeSeriesView( + origin=entity_path, + name="Action", + time_ranges=[ + rrb.VisibleTimeRange( + "frame", + start=rrb.TimeRangeBoundary.cursor_relative(seq=-self.idxrangeboundary), + end=rrb.TimeRangeBoundary.cursor_relative(), + ) + ], + plot_legend=rrb.PlotLegend(visible=True), + ) + ) + + if not views: + print("Warning: No visualizable components detected in the data.") + return + + grid = rrb.Grid(contents=views) + rr.send_blueprint(grid) + self.blueprint_sent = True + + def log_step(self, step_data: dict[str, Any]): + """Logs a single step dictionary from your dataset.""" + if not self.blueprint_sent: + self._initialize_from_data(step_data) + + if self._index_key in step_data: + current_index = step_data[self._index_key].item() + rr.set_time_sequence("frame", current_index) + + episode_idx = step_data.get(self._episode_index_key, torch.tensor(-1)).item() + if episode_idx != self.current_episode: + self.current_episode = episode_idx + task_name = step_data.get(self._task_key, "Unknown Task") + log_text = f"Starting Episode {self.current_episode}: {task_name}" + rr.log(f"{self.prefix}info/task", rr.TextLog(log_text, level=rr.TextLogLevel.INFO)) + + for key in self._image_keys: + if key in step_data: + image_tensor = step_data[key] + if image_tensor.ndim > 2: + clean_name = key.replace("observation.images.", "") + entity_path = f"{self.prefix}images/{clean_name}" + if image_tensor.shape[0] in [1, 3, 4]: + image_tensor = image_tensor.permute(1, 2, 0) + rr.log(entity_path, rr.Image(image_tensor)) + + if self._state_key in step_data: + state_tensor = step_data[self._state_key] + entity_path = f"{self.prefix}state" + for i, val in enumerate(state_tensor): + rr.log(f"{entity_path}/joint_{i}", rr.Scalar(val.item())) + + if self._action_key in step_data: + action_tensor = step_data[self._action_key] + entity_path = f"{self.prefix}action" + for i, val in enumerate(action_tensor): + rr.log(f"{entity_path}/joint_{i}", rr.Scalar(val.item())) + + +def visualization_data(idx, observation, state, action, online_logger): + item_data: dict[str, Any] = { + "index": torch.tensor(idx), + "observation.state": state, + "action": action, + } + for k, v in observation.items(): + if k not in ("index", "observation.state", "action"): + item_data[k] = v + online_logger.log_step(item_data) diff --git a/eval_robot/utils/sim_savedata_utils.py b/eval_robot/utils/sim_savedata_utils.py new file mode 100644 index 000000000..97f82adde --- /dev/null +++ b/eval_robot/utils/sim_savedata_utils.py @@ -0,0 +1,209 @@ +# for simulation +import torch +import numpy as np +import logging_mp +from unitree_lerobot.eval_robot.utils.utils import ( + reset_policy, +) +from unitree_lerobot.eval_robot.make_robot import ( + publish_reset_category, +) +from dataclasses import dataclass +from lerobot.configs import parser +from lerobot.configs.policies import PreTrainedConfig +import time + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def process_data_add(episode_writer, observation_image, current_arm_q, ee_state, action, arm_dof, ee_dof): + if episode_writer is None: + return + if ( + observation_image is not None + and current_arm_q is not None + and ee_state is not None + and action is not None + and arm_dof is not None + and ee_dof is not None + ): + # Convert tensors to numpy arrays for JSON serialization + if torch.is_tensor(current_arm_q): + current_arm_q = current_arm_q.detach().cpu().numpy() + if torch.is_tensor(ee_state): + ee_state = ee_state.detach().cpu().numpy() + if torch.is_tensor(action): + action = action.detach().cpu().numpy() + colors = {} + i = 0 + for key, value in observation_image.items(): + if "images" in key: + if value is not None: + # Convert PyTorch tensor to numpy array for OpenCV compatibility + if torch.is_tensor(value): + # Convert tensor to numpy array and ensure correct format for OpenCV + img_array = value.detach().cpu().numpy() + # If the image is in CHW format (channels first), convert to HWC format (channels last) + if img_array.ndim == 3 and img_array.shape[0] in [1, 3, 4]: + img_array = np.transpose(img_array, (1, 2, 0)) + # Ensure the array is in uint8 format for OpenCV + if img_array.dtype != np.uint8: + if img_array.max() <= 1.0: # Normalized values [0, 1] + img_array = (img_array * 255).astype(np.uint8) + else: # Values already in [0, 255] range + img_array = img_array.astype(np.uint8) + # Keep original RGB format - no color channel conversion needed + colors[f"color_{i}"] = img_array + else: + colors[f"color_{i}"] = value + i += 1 + states = { + "left_arm": { + "qpos": current_arm_q[: arm_dof // 2].tolist(), # numpy.array -> list + "qvel": [], + "torque": [], + }, + "right_arm": { + "qpos": current_arm_q[arm_dof // 2 :].tolist(), + "qvel": [], + "torque": [], + }, + "left_ee": { + "qpos": ee_state[:ee_dof].tolist(), + "qvel": [], + "torque": [], + }, + "right_ee": { + "qpos": ee_state[ee_dof:].tolist(), + "qvel": [], + "torque": [], + }, + "body": { + "qpos": [], + }, + } + actions = { + "left_arm": { + "qpos": action[: arm_dof // 2].tolist(), + "qvel": [], + "torque": [], + }, + "right_arm": { + "qpos": action[arm_dof // 2 :].tolist(), + "qvel": [], + "torque": [], + }, + "left_ee": { + "qpos": action[arm_dof : arm_dof + ee_dof].tolist(), + "qvel": [], + "torque": [], + }, + "right_ee": { + "qpos": action[arm_dof + ee_dof : arm_dof + 2 * ee_dof].tolist(), + "qvel": [], + "torque": [], + }, + "body": { + "qpos": [], + }, + } + episode_writer.add_item(colors, states=states, actions=actions) + + +def process_data_save(episode_writer, result): + """Processes data and saves it.""" + if episode_writer is None: + return + episode_writer.save_episode(result) + + +def is_success( + sim_reward_subscriber, + episode_writer, + reset_pose_publisher, + policy, + cfg, + reward_stats, + init_arm_pose, + robot_interface, +): + # logger_mp.info(f"arm_action {arm_action}, tau {tau}") + if sim_reward_subscriber: + data = sim_reward_subscriber.read_data() + if data is not None: + if int(data["rewards"][0]) == 1: + reward_stats["reward_sum"] += 1 + sim_reward_subscriber.reset_data() + # success + if reward_stats["reward_sum"] >= 25: + process_data_save(episode_writer, "success") + logger_mp.info( + f"Episode {reward_stats['episode_num']} finished with reward {reward_stats['reward_sum']},save data..." + ) + reward_stats["episode_num"] = -1 + reward_stats["reward_sum"] = 0 + time.sleep(1) + publish_reset_category(1, reset_pose_publisher) + time.sleep(1) + reset_policy(policy) + sim_reward_subscriber.reset_data() + # fail + elif reward_stats["episode_num"] > cfg.max_episodes: + process_data_save(episode_writer, "fail") + logger_mp.info(f"Episode {reward_stats['episode_num']} finished with reward {reward_stats['reward_sum']}") + reward_stats["episode_num"] = -1 + reward_stats["reward_sum"] = 0 + reset_policy(policy) + sim_reward_subscriber.reset_data() + logger_mp.info("Initializing robot to starting pose...") + tau = robot_interface["arm_ik"].solve_tau(init_arm_pose) + robot_interface["arm_ctrl"].ctrl_dual_arm(init_arm_pose, tau) + time.sleep(1) + publish_reset_category(1, reset_pose_publisher) + time.sleep(1) + reset_policy(policy) + sim_reward_subscriber.reset_data() + time.sleep(1) + + +@dataclass +class EvalRealConfig: + repo_id: str + policy: PreTrainedConfig | None = None + + root: str = "" + episodes: int = 0 + frequency: float = 30.0 + + # Basic control parameters + arm: str = "G1_29" # G1_29, G1_23 + ee: str = "dex3" # dex3, dex1, inspire1, brainco + + # Mode flags + motion: bool = False + headless: bool = False + sim: bool = True + visualization: bool = False + send_real_robot: bool = False + use_dataset: bool = False + save_data: bool = False + task_dir: str = "./data" + max_episodes: int = 1200 + + def __post_init__(self): + # HACK: We parse again the cli args here to get the pretrained path if there was one. + policy_path = parser.get_path_arg("policy") + if policy_path: + cli_overrides = parser.get_cli_overrides("policy") + self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides) + self.policy.pretrained_path = policy_path + else: + logger_mp.warning( + "No pretrained path was provided, evaluated policy will be built from scratch (random weights)." + ) + + @classmethod + def __get_path_fields__(cls) -> list[str]: + """This enables the parser to load config from the policy using `--policy.path=local/dir`""" + return ["policy"] diff --git a/eval_robot/utils/sim_state_topic.py b/eval_robot/utils/sim_state_topic.py new file mode 100644 index 000000000..110280baa --- /dev/null +++ b/eval_robot/utils/sim_state_topic.py @@ -0,0 +1,402 @@ +# Copyright (c) 2025, Unitree Robotics Co., Ltd. All Rights Reserved. +# License: Apache License, Version 2.0 +""" +Simple sim state subscriber class +Subscribe to rt/sim_state_cmd topic and write to shared memory +""" + +import threading +import time +import json +from multiprocessing import shared_memory +from typing import Any +from unitree_sdk2py.core.channel import ChannelSubscriber +from unitree_sdk2py.idl.std_msgs.msg.dds_ import String_ + +import logging_mp + +logger_mp = logging_mp.get_logger(__name__) + + +class SharedMemoryManager: + """Shared memory manager""" + + def __init__(self, name: str | None = None, size: int = 512): + """Initialize shared memory manager + + Args: + name: shared memory name, if None, create new one + size: shared memory size (bytes) + """ + self.size = size + self.lock = threading.RLock() # reentrant lock + + if name: + try: + self.shm = shared_memory.SharedMemory(name=name) + self.shm_name = name + self.created = False + except FileNotFoundError: + self.shm = shared_memory.SharedMemory(create=True, size=size) + self.shm_name = self.shm.name + self.created = True + else: + self.shm = shared_memory.SharedMemory(create=True, size=size) + self.shm_name = self.shm.name + self.created = True + + def write_data(self, data: dict[str, Any]) -> bool: + """Write data to shared memory + + Args: + data: data to write + + Returns: + bool: write success or not + """ + try: + with self.lock: + json_str = json.dumps(data) + json_bytes = json_str.encode("utf-8") + + if len(json_bytes) > self.size - 8: # reserve 8 bytes for length and timestamp + logger_mp.warning(f"Data too large for shared memory ({len(json_bytes)} > {self.size - 8})") + return False + + # write timestamp (4 bytes) and data length (4 bytes) + timestamp = int(time.time()) & 0xFFFFFFFF # 32-bit timestamp, use bitmask to ensure in range + self.shm.buf[0:4] = timestamp.to_bytes(4, "little") + self.shm.buf[4:8] = len(json_bytes).to_bytes(4, "little") + + # write data + self.shm.buf[8 : 8 + len(json_bytes)] = json_bytes + return True + + except Exception as e: + logger_mp.error(f"Error writing to shared memory: {e}") + return False + + def read_data(self) -> dict[str, Any] | None: + """Read data from shared memory + + Returns: + Dict[str, Any]: read data dictionary, return None if failed + """ + try: + with self.lock: + # read timestamp and data length + timestamp = int.from_bytes(self.shm.buf[0:4], "little") + data_len = int.from_bytes(self.shm.buf[4:8], "little") + + if data_len == 0: + return None + + # read data + json_bytes = bytes(self.shm.buf[8 : 8 + data_len]) + data = json.loads(json_bytes.decode("utf-8")) + data["_timestamp"] = timestamp # add timestamp information + return data + + except Exception as e: + logger_mp.error(f"Error reading from shared memory: {e}") + return None + + def reset_data(self): + """Reset data""" + if self.shm: + self.shm.buf[0:8] = b"\x00" * 8 + else: + logger_mp.error("[SharedMemoryManager] Shared memory is not initialized") + + def get_name(self) -> str: + """Get shared memory name""" + return self.shm_name + + def cleanup(self): + """Clean up shared memory""" + if hasattr(self, "shm") and self.shm: + self.shm.close() + if self.created: + self.shm.unlink() + + def __del__(self): + """Destructor""" + self.cleanup() + + +class SimStateSubscriber: + """Simple sim state subscriber class""" + + def __init__(self, shm_name: str = "sim_state_cmd_data", shm_size: int = 4096): + """Initialize the subscriber + + Args: + shm_name: shared memory name + shm_size: shared memory size + """ + self.shm_name = shm_name + self.shm_size = shm_size + self.running = False + self.subscriber = None + self.subscribe_thread = None + self.shared_memory = None + + # initialize shared memory + self._setup_shared_memory() + + logger_mp.debug(f"[SimStateSubscriber] Initialized with shared memory: {shm_name}") + + def _setup_shared_memory(self): + """Setup shared memory""" + try: + self.shared_memory = SharedMemoryManager(self.shm_name, self.shm_size) + logger_mp.debug("[SimStateSubscriber] Shared memory setup successfully") + except Exception as e: + logger_mp.error(f"[SimStateSubscriber] Failed to setup shared memory: {e}") + + def start_subscribe(self): + """Start subscribing""" + if self.running: + logger_mp.warning("[SimStateSubscriber] Already running") + return + + try: + self.subscriber = ChannelSubscriber("rt/sim_state", String_) + self.subscriber.Init() + self.running = True + + self.subscribe_thread = threading.Thread(target=self._subscribe_sim_state, daemon=True) + self.subscribe_thread.start() + + logger_mp.info("[SimStateSubscriber] Started subscribing to rt/sim_state") + + except Exception as e: + logger_mp.error(f"[SimStateSubscriber] Failed to start subscribing: {e}") + self.running = False + + def _subscribe_sim_state(self): + """Subscribe loop thread""" + logger_mp.debug("[SimStateSubscriber] Subscribe thread started") + + while self.running: + try: + if self.subscriber: + msg = self.subscriber.Read() + if msg: + data = json.loads(msg.data) + else: + logger_mp.warning("[SimStateSubscriber] Received None message") + if self.shared_memory and data: + self.shared_memory.write_data(data) + else: + logger_mp.error("[SimStateSubscriber] Subscriber is not initialized") + time.sleep(0.002) + except Exception as e: + logger_mp.error(f"[SimStateSubscriber] Error in subscribe loop: {e}") + time.sleep(0.01) + + def stop_subscribe(self): + """Stop subscribing""" + if not self.running: + logger_mp.warning("[SimStateSubscriber] Already stopped or not running") + return + + self.running = False + # wait for thread to finish + if self.subscribe_thread: + self.subscribe_thread.join(timeout=1.0) + + if self.shared_memory: + self.shared_memory.cleanup() + logger_mp.info("[SimStateSubscriber] Subscriber stopped") + + def read_data(self) -> dict[str, Any] | None: + """Read data from shared memory + + Returns: + Dict: received data, None if no data or error + """ + try: + if self.shared_memory: + return self.shared_memory.read_data() + return None + except Exception as e: + logger_mp.error(f"[SimStateSubscriber] Error reading data: {e}") + return None + + def is_running(self) -> bool: + """Check if subscriber is running""" + return self.running + + def __del__(self): + """Destructor""" + self.stop_subscribe() + + +def start_sim_state_subscribe(shm_name: str = "sim_state_cmd_data", shm_size: int = 4096) -> SimStateSubscriber: + """Start sim state subscribing + + Args: + shm_name: shared memory name + shm_size: shared memory size + + Returns: + SimStateSubscriber: started subscriber instance + """ + subscriber = SimStateSubscriber(shm_name, shm_size) + subscriber.start_subscribe() + return subscriber + + +# ============================== sim reward topic ============================== +class SimRewardSubscriber: + """Simple sim state subscriber class""" + + def __init__(self, shm_name: str = "sim_reward_cmd_data", shm_size: int = 256): + """Initialize the subscriber + + Args: + shm_name: shared memory name + shm_size: shared memory size + """ + self.shm_name = shm_name + self.shm_size = shm_size + self.running = False + self.subscriber = None + self.subscribe_thread = None + self.shared_memory = None + + # initialize shared memory + self._setup_shared_memory() + + logger_mp.debug(f"[SimRewardSubscriber] Initialized with shared memory: {shm_name}") + + def _setup_shared_memory(self): + """Setup shared memory""" + try: + self.shared_memory = SharedMemoryManager(self.shm_name, self.shm_size) + logger_mp.debug("[SimRewardSubscriber] Shared memory setup successfully") + except Exception as e: + logger_mp.error(f"[SimRewardSubscriber] Failed to setup shared memory: {e}") + + def start_subscribe(self): + """Start subscribing""" + if self.running: + logger_mp.warning("[SimRewardSubscriber] Already running") + return + + try: + self.subscriber = ChannelSubscriber("rt/rewards_state", String_) + self.subscriber.Init() + self.running = True + + self.subscribe_thread = threading.Thread(target=self._subscribe_sim_reward, daemon=True) + self.subscribe_thread.start() + + logger_mp.info("[SimRewardSubscriber] Started subscribing to rt/sim_reward") + + except Exception as e: + logger_mp.error(f"[SimRewardSubscriber] Failed to start subscribing: {e}") + self.running = False + + def _subscribe_sim_reward(self): + """Subscribe loop thread""" + logger_mp.debug("[SimRewardSubscriber] Subscribe thread started") + + while self.running: + try: + if self.subscriber: + msg = self.subscriber.Read() + if msg: + data = json.loads(msg.data) + else: + logger_mp.warning("[SimRewardSubscriber] Received None message") + if self.shared_memory and data: + self.shared_memory.write_data(data) + else: + logger_mp.error("[SimRewardSubscriber] Subscriber is not initialized") + time.sleep(0.01) + except Exception as e: + logger_mp.error(f"[SimRewardSubscriber] Error in subscribe loop: {e}") + time.sleep(0.02) + + def stop_subscribe(self): + """Stop subscribing""" + if not self.running: + logger_mp.warning("[SimRewardSubscriber] Already stopped or not running") + return + + self.running = False + # wait for thread to finish + if self.subscribe_thread: + self.subscribe_thread.join(timeout=1.0) + + if self.shared_memory: + self.shared_memory.cleanup() + logger_mp.info("[SimRewardSubscriber] Subscriber stopped") + + def read_data(self) -> dict[str, Any] | None: + """Read data from shared memory + + Returns: + Dict: received data, None if no data or error + """ + try: + if self.shared_memory: + return self.shared_memory.read_data() + return None + except Exception as e: + logger_mp.error(f"[SimRewardSubscriber] Error reading data: {e}") + return None + + def reset_data(self): + """Reset data""" + if self.shared_memory: + data = {"rewards": [0.0], "timestamp": 1758009108.266387} + self.shared_memory.write_data(data) + + def is_running(self) -> bool: + """Check if subscriber is running""" + return self.running + + def __del__(self): + """Destructor""" + self.stop_subscribe() + + +# ============================== sim reward topic ============================== +def start_sim_reward_subscribe(shm_name: str = "sim_reward_cmd_data", shm_size: int = 256) -> SimRewardSubscriber: + """Start sim reward subscribing + + Args: + shm_name: shared memory name + shm_size: shared memory size + + Returns: + SimRewardSubscriber: started subscriber instance + """ + subscriber = SimRewardSubscriber(shm_name, shm_size) + subscriber.start_subscribe() + return subscriber + + +# if __name__ == "__main__": +# # example usage +# logger_mp.info("Starting sim state subscriber...") +# ChannelFactoryInitialize(0) +# # create and start subscriber +# subscriber = start_sim_state_subscribe() + +# try: +# # keep running and check for data +# while True: +# data = subscriber.read_data() +# if data: +# logger_mp.info(f"Read data: {data}") +# time.sleep(1) + +# except KeyboardInterrupt: +# logger_mp.warning("\nInterrupted by user") +# finally: +# subscriber.stop_subscribe() +# logger_mp.info("Subscriber stopped") diff --git a/eval_robot/utils/utils.py b/eval_robot/utils/utils.py new file mode 100644 index 000000000..36d2c44d3 --- /dev/null +++ b/eval_robot/utils/utils.py @@ -0,0 +1,142 @@ +import numpy as np +import torch +from typing import Any +from contextlib import nullcontext +from copy import copy +import logging +from dataclasses import dataclass +from lerobot.configs import parser +from lerobot.configs.policies import PreTrainedConfig +from lerobot.policies.pretrained import PreTrainedPolicy + + +import logging_mp + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def extract_observation(step: dict): + observation = {} + + for key, value in step.items(): + if key.startswith("observation.images."): + if isinstance(value, np.ndarray) and value.ndim == 3 and value.shape[-1] in [1, 3]: + value = np.transpose(value, (2, 0, 1)) + observation[key] = value + + elif key == "observation.state": + observation[key] = value + + return observation + + +def predict_action( + observation: dict[str, np.ndarray], + policy: PreTrainedPolicy, + device: torch.device, + use_amp: bool, + task: str | None = None, + use_dataset: bool | None = False, +): + observation = copy(observation) + with ( + torch.inference_mode(), + torch.autocast(device_type=device.type) if device.type == "cuda" and use_amp else nullcontext(), + ): + # Convert to pytorch format: channel first and float32 in [0,1] with batch dimension + for name in observation: + if not use_dataset: + # Skip non-tensor observations (like task strings) + if not hasattr(observation[name], "unsqueeze"): + continue + if "images" in name: + observation[name] = observation[name].type(torch.float32) / 255 + observation[name] = observation[name].permute(2, 0, 1).contiguous() + + observation[name] = observation[name].unsqueeze(0).to(device) + + observation["task"] = [task if task else ""] + + # Compute the next action with the policy + # based on the current observation + action = policy.select_action(observation) + + # Remove batch dimension + action = action.squeeze(0) + + # Move to cpu, if not already the case + action = action.to("cpu") + + return action + + +def reset_policy(policy: PreTrainedPolicy): + policy.reset() + + +def cleanup_resources(image_info: dict[str, Any]): + """Safely close and unlink shared memory resources.""" + logger_mp.info("Cleaning up shared memory resources.") + for shm in image_info["shm_resources"]: + if shm: + shm.close() + shm.unlink() + + +def to_list(x): + if torch is not None and isinstance(x, torch.Tensor): + return x.detach().cpu().ravel().tolist() + if isinstance(x, np.ndarray): + return x.ravel().tolist() + if isinstance(x, (list, tuple)): + return list(x) + return [x] + + +def to_scalar(x): + if torch is not None and isinstance(x, torch.Tensor): + return float(x.detach().cpu().ravel()[0].item()) + if isinstance(x, np.ndarray): + return float(x.ravel()[0]) + if isinstance(x, (list, tuple)): + return float(x[0]) + return float(x) + + +@dataclass +class EvalRealConfig: + repo_id: str + policy: PreTrainedConfig | None = None + + root: str = "" + episodes: int = 0 + frequency: float = 30.0 + + # Basic control parameters + arm: str = "G1_29" # G1_29, G1_23 + ee: str = "dex3" # dex3, dex1, inspire1, brainco + + # Mode flags + motion: bool = False + headless: bool = False + visualization: bool = False + send_real_robot: bool = False + use_dataset: bool = False + + def __post_init__(self): + # HACK: We parse again the cli args here to get the pretrained path if there was one. + policy_path = parser.get_path_arg("policy") + if policy_path: + cli_overrides = parser.get_cli_overrides("policy") + self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides) + self.policy.pretrained_path = policy_path + else: + logging.warning( + "No pretrained path was provided, evaluated policy will be built from scratch (random weights)." + ) + + @classmethod + def __get_path_fields__(cls) -> list[str]: + """This enables the parser to load config from the policy using `--policy.path=local/dir`""" + return ["policy"] diff --git a/eval_robot/utils/weighted_moving_filter.py b/eval_robot/utils/weighted_moving_filter.py new file mode 100644 index 000000000..20bf8806e --- /dev/null +++ b/eval_robot/utils/weighted_moving_filter.py @@ -0,0 +1,99 @@ +import numpy as np +import matplotlib.pyplot as plt + + +class WeightedMovingFilter: + def __init__(self, weights, data_size=14): + self._window_size = len(weights) + self._weights = np.array(weights) + # assert np.isclose(np.sum(self._weights), 1.0), "[WeightedMovingFilter] the sum of weights list must be 1.0!" + self._data_size = data_size + self._filtered_data = np.zeros(self._data_size) + self._data_queue = [] + + def _apply_filter(self): + if len(self._data_queue) < self._window_size: + return self._data_queue[-1] + + data_array = np.array(self._data_queue) + temp_filtered_data = np.zeros(self._data_size) + for i in range(self._data_size): + temp_filtered_data[i] = np.convolve(data_array[:, i], self._weights, mode="valid")[-1] + + return temp_filtered_data + + def add_data(self, new_data): + assert len(new_data) == self._data_size + + if len(self._data_queue) > 0 and np.array_equal(new_data, self._data_queue[-1]): + return # skip duplicate data + + if len(self._data_queue) >= self._window_size: + self._data_queue.pop(0) + + self._data_queue.append(new_data) + self._filtered_data = self._apply_filter() + + @property + def filtered_data(self): + return self._filtered_data + + +def visualize_filter_comparison(filter_params, steps): + import time + + t = np.linspace(0, 4 * np.pi, steps) + original_data = np.array( + [np.sin(t + i) + np.random.normal(0, 0.2, len(t)) for i in range(35)] + ).T # sin wave with noise, shape is [len(t), 35] + + plt.figure(figsize=(14, 10)) + + for idx, weights in enumerate(filter_params): + filter = WeightedMovingFilter(weights, 14) + data_2b_filtered = original_data.copy() + filtered_data = [] + + time1 = time.time() + + for i in range(steps): + filter.add_data(data_2b_filtered[i][13:27]) # step i, columns 13 to 26 (total:14) + data_2b_filtered[i][13:27] = filter.filtered_data + filtered_data.append(data_2b_filtered[i]) + + time2 = time.time() + print(f"filter_params:{filter_params[idx]}, time cosume:{time2 - time1}") + + filtered_data = np.array(filtered_data) + + # col0 should not 2b filtered + plt.subplot(len(filter_params), 2, idx * 2 + 1) + plt.plot(filtered_data[:, 0], label=f"Filtered (Window {filter._window_size})") + plt.plot(original_data[:, 0], "r--", label="Original", alpha=0.5) + plt.title("Joint 1 - Should not to be filtered.") + plt.xlabel("Step") + plt.ylabel("Value") + plt.legend() + + # col13 should 2b filtered + plt.subplot(len(filter_params), 2, idx * 2 + 2) + plt.plot(filtered_data[:, 13], label=f"Filtered (Window {filter._window_size})") + plt.plot(original_data[:, 13], "r--", label="Original", alpha=0.5) + plt.title(f"Joint 13 - Window {filter._window_size}, Weights {weights}") + plt.xlabel("Step") + plt.ylabel("Value") + plt.legend() + + plt.tight_layout() + plt.show() + + +if __name__ == "__main__": + # windows_size and weights + filter_params = [ + (np.array([0.7, 0.2, 0.1])), + (np.array([0.5, 0.3, 0.2])), + (np.array([0.4, 0.3, 0.2, 0.1])), + ] + + visualize_filter_comparison(filter_params, steps=100) diff --git a/get_calibration.py b/get_calibration.py new file mode 100644 index 000000000..0e879c187 --- /dev/null +++ b/get_calibration.py @@ -0,0 +1,107 @@ +import json +import time +import math +from pathlib import Path + +# ---- key → (section, name, id) +MAP = { + # LEFT + "kLeftShoulderPitch.pos": ("left", "shoulder_pitch", 0), + "kLeftShoulderYaw.pos": ("left", "shoulder_yaw", 1), + "kLeftShoulderRoll.pos": ("left", "shoulder_roll", 2), + "kLeftElbow.pos": ("left", "elbow_flex", 3), + "kLeftWristRoll.pos": ("left", "wrist_roll", 4), + "kLeftWristYaw.pos": ("left", "wrist_yaw", 5), + "kLeftWristyaw.pos": ("left", "wrist_yaw", 5), # tolerate casing variant + "kLeftWristPitch.pos": ("left", "wrist_pitch", 6), + + # RIGHT + "kRightShoulderPitch.pos": ("right", "shoulder_pitch", 0), + "kRightShoulderYaw.pos": ("right", "shoulder_yaw", 1), + "kRightShoulderRoll.pos": ("right", "shoulder_roll", 2), + "kRightElbow.pos": ("right", "elbow_flex", 3), + "kRightWristRoll.pos": ("right", "wrist_roll", 4), + "kRightWristYaw.pos": ("right", "wrist_yaw", 5), + "kRightWristPitch.pos": ("right", "wrist_pitch", 6), +} + +# Output +CALIB_PATH = Path("calibration.json") +ROUND_TO_INT = False # set True if you want int ranges + +# Init tracker: tracker["left"]["shoulder_pitch"] = {...} +tracker = {"left": {}, "right": {}} +for sec, name, idx in MAP.values(): + if name not in tracker[sec]: + tracker[sec][name] = { + "id": idx, + "drive_mode": 0, + "homing_offset": 0, + "range_min": math.inf, + "range_max": -math.inf, + } + +def _to_float(x): + # unwrap numpy / torch scalars if present + if hasattr(x, "item"): + try: + x = x.item() + except Exception: + pass + return float(x) + +def update_tracker(obs: dict): + for k, v in obs.items(): + if k not in MAP: + continue + sec, name, _ = MAP[k] + try: + x = _to_float(v) + except Exception: + continue + t = tracker[sec][name] + if x < t["range_min"]: + t["range_min"] = x + if x > t["range_max"]: + t["range_max"] = x + +def dump_calibration(path: Path): + out = {"left": {}, "right": {}} + for sec in ("left", "right"): + for name, d in tracker[sec].items(): + mn, mx = d["range_min"], d["range_max"] + if ROUND_TO_INT: + mn = None if mn is math.inf else int(round(mn)) + mx = None if mx is -math.inf else int(round(mx)) + else: + mn = None if mn is math.inf else mn + mx = None if mx is -math.inf else mx + out[sec][name] = { + "id": d["id"], + "drive_mode": d["drive_mode"], + "homing_offset": d["homing_offset"], + "range_min": mn, + "range_max": mx, + } + path.write_text(json.dumps(out, indent=4)) + print(f"Saved calibration to {path.resolve()}") + +from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1, G1_29_JointIndex +from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config + +from lerobot.datasets.lerobot_dataset import LeRobotDataset +import time +config = UnitreeG1Config( + motion_mode=False, + simulation_mode=False +) + +robot = UnitreeG1(config) +try: + while True: + observation = robot.get_observation() + update_tracker(observation) + robot.send_action(observation) # mirror, if desired + time.sleep(0.01) +except KeyboardInterrupt: + dump_calibration(CALIB_PATH) diff --git a/mujoco_sim_g1/CAMERA_README.md b/mujoco_sim_g1/CAMERA_README.md new file mode 100644 index 000000000..dd407a876 --- /dev/null +++ b/mujoco_sim_g1/CAMERA_README.md @@ -0,0 +1,256 @@ +# Camera System for MuJoCo G1 Simulator + +## Overview + +The simulator has two cameras defined: + +### 1. **`head_camera`** - Robot Ego-View +- **Location**: Attached to `torso_link` body +- **Position**: `[0.06, 0.0, 0.45]` relative to torso (6cm forward, 45cm up) +- **Orientation**: `euler="0 -0.8 -1.57"` (facing forward, slightly tilted down) +- **FOV**: 90 degrees +- **Purpose**: First-person view from the robot's perspective (like a head-mounted camera) + +### 2. **`global_view`** - Third-Person View +- **Location**: Fixed in world coordinates +- **Position**: `[2.910, -5.040, 3.860]` (behind and above the robot) +- **Purpose**: External observer view for visualization + +## How Camera Publishing Works + +The camera system uses a **zero-copy architecture** with three components: + +``` +┌─────────────────────┐ +│ MuJoCo Simulator │ +│ (Main Process) │ +│ │ +│ 1. Render cameras │ +│ 2. Copy to shmem │──┐ +└─────────────────────┘ │ + │ Shared Memory + │ (fast IPC) + ┌────▼────────────────┐ + │ Image Publisher │ + │ (Subprocess) │ + │ │ + │ 3. Encode images │ + │ 4. ZMQ publish │ + └─────────┬───────────┘ + │ + │ TCP (ZMQ) + │ port 5555 + ▼ + ┌─────────────────────┐ + │ Your Policy/Client │ + │ (Subscribe) │ + └─────────────────────┘ +``` + +### Key Technologies: +- **MuJoCo Renderer**: Captures RGB images from virtual cameras +- **Shared Memory (`multiprocessing.shared_memory`)**: Zero-copy transfer between processes +- **ZMQ (ZeroMQ)**: Network socket for publishing images (TCP) +- **No ROS2 required!** Pure Python multiprocessing + +## Usage + +### Basic Simulation (No Camera Publishing) +```bash +python run_sim.py +``` + +### With Camera Publishing +```bash +# Publish head camera on default port 5555 +python run_sim.py --publish-images + +# Publish multiple cameras +python run_sim.py --publish-images --cameras head_camera global_view + +# Custom port +python run_sim.py --publish-images --camera-port 6000 +``` + +### Viewing Camera Streams + +In a **separate terminal**, run the camera viewer: + +```bash +# Basic usage (default: localhost:5555) +python view_cameras.py + +# Custom host/port +python view_cameras.py --host 192.168.1.100 --port 6000 + +# Save images to directory +python view_cameras.py --save ./camera_recordings + +# Adjust display rate +python view_cameras.py --fps 60 +``` + +**Keyboard Controls:** +- `q`: Quit viewer +- `s`: Save snapshot of current frame + +**Example Workflow:** +```bash +# Terminal 1: Start simulator with camera publishing +python run_sim.py --publish-images + +# Terminal 2: View the camera feed +python view_cameras.py +``` + +### Receiving Images in Your Code + +```python +import zmq +import numpy as np +from gr00t_wbc.control.sensor.sensor_server import ImageMessageSchema + +# Connect to camera publisher +context = zmq.Context() +socket = context.socket(zmq.SUB) +socket.connect("tcp://localhost:5555") +socket.setsockopt(zmq.SUBSCRIBE, b"") # Subscribe to all messages + +while True: + # Receive serialized image data + data = socket.recv_pyobj() + + # Decode images + if "head_camera" in data: + # Decode image (returns numpy array HxWx3 uint8) + img = decode_image(data["head_camera"]) + + # Use image for your policy + process_observation(img) +``` + +## Camera Configuration + +Edit `config.yaml` to change camera settings: + +```yaml +IMAGE_DT: 0.033333 # Publishing rate (30 Hz) +ENABLE_OFFSCREEN: false # Enable for camera rendering +MP_START_METHOD: "spawn" # Multiprocessing method +``` + +Or programmatically in `run_sim.py`: + +```python +camera_configs = { + "head_camera": { + "height": 480, + "width": 640 + }, + "custom_camera": { + "height": 224, + "width": 224 + } +} +``` + +## Adding Custom Cameras + +Edit `assets/g1_29dof_with_hand.xml` or `assets/scene_43dof.xml`: + +```xml + + + + + + + + + +``` + +Then publish it: +```bash +python run_sim.py --publish-images --cameras my_camera +``` + +## Performance Notes + +- **Rendering overhead**: ~5-10ms per camera per frame @ 640x480 +- **Publishing overhead**: ~2-3ms for encoding + network +- Image publishing runs in **separate subprocess** to not block simulation +- Uses **shared memory** for fast inter-process image transfer +- Target: 30 FPS camera publishing while maintaining 500 Hz simulation + +## Troubleshooting + +### No images received? +1. Check if offscreen rendering is enabled (`--publish-images` flag) +2. Verify ZMQ port is not blocked +3. Check camera exists in scene XML + +### Images are delayed? +- Reduce `IMAGE_DT` in config +- Lower camera resolution +- Use fewer cameras + +### "Camera not found" error? +- Verify camera name in XML matches config +- Check XML syntax is valid +- Ensure MuJoCo model loads successfully + +## Quick Reference + +### File Structure +``` +mujoco_sim_g1/ +├── run_sim.py # Simulator launcher +├── view_cameras.py # Camera viewer (this file!) +├── config.yaml # Simulator config +├── assets/ +│ ├── scene_43dof.xml # Scene with global_view camera +│ └── g1_29dof_with_hand.xml # Robot model with head_camera +└── sim/ + ├── base_sim.py # MuJoCo environment + ├── sensor_utils.py # ZMQ camera server/client + └── image_publish_utils.py # Multiprocessing image publisher +``` + +### Camera Definitions + +Edit these files to modify cameras: + +**`assets/g1_29dof_with_hand.xml`** - Robot-attached cameras: +```xml + + + +``` + +**`assets/scene_43dof.xml`** - World-frame cameras: +```xml + + + +``` + +### Complete Example + +```bash +# Terminal 1: Start simulator with camera publishing +cd mujoco_sim_g1 +python run_sim.py --publish-images --cameras head_camera global_view + +# Terminal 2: View cameras in real-time +python view_cameras.py + +# Terminal 3: Use in your policy (Python code) +from sim.sensor_utils import SensorClient, ImageUtils +client = SensorClient() +client.start_client("localhost", 5555) +data = client.receive_message() +img = ImageUtils.decode_image(data["head_camera"]) +# img is now numpy array (H, W, 3) in BGR format +``` + diff --git a/mujoco_sim_g1/MUJOCO_LOG.TXT b/mujoco_sim_g1/MUJOCO_LOG.TXT new file mode 100644 index 000000000..daa3c30e6 --- /dev/null +++ b/mujoco_sim_g1/MUJOCO_LOG.TXT @@ -0,0 +1,3 @@ +Thu Nov 20 17:42:09 2025 +WARNING: Nan, Inf or huge value in QACC at DOF 0. The simulation is unstable. Time = 124.1560. + diff --git a/mujoco_sim_g1/README.md b/mujoco_sim_g1/README.md new file mode 100644 index 000000000..3fa6b01e1 --- /dev/null +++ b/mujoco_sim_g1/README.md @@ -0,0 +1,167 @@ +# Standalone MuJoCo Simulator for Unitree G1 + +This is a standalone MuJoCo physics simulator for the Unitree G1 robot, extracted from the GR00T-WholeBodyControl repository. + +## Features + +- **Physics Simulation**: Runs G1 robot in MuJoCo at 500Hz (2ms timestep) +- **DDS Communication**: Uses Unitree SDK2 DDS for robot communication +- **Compatible**: Works with existing `unitree_g1.py` control code via DDS +- **Visualization**: Real-time 3D visualization of robot motion + +## Directory Structure + +``` +mujoco_sim_g1/ +├── requirements.txt # Python dependencies +├── run_sim.py # Main launcher script +├── config.yaml # Simulation configuration +├── sim/ # Simulation modules +│ ├── base_sim.py +│ ├── simulator_factory.py +│ ├── unitree_sdk2py_bridge.py +│ └── ... +└── assets/ # Robot models + ├── g1_29dof_with_hand.xml + └── meshes/*.STL +``` + +## Installation + +### 1. Create Virtual Environment (Recommended) + +```bash +cd mujoco_sim_g1 +python3 -m venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate +``` + +### 2. Install Dependencies + +```bash +pip install -r requirements.txt +``` + +**Note**: If you encounter issues with `rclpy`, you can comment out the ROS2 imports in `sim/base_sim.py` (lines 11, 609-617) if you don't need camera publishing. + +## Usage + +### Basic Usage + +```bash +# Activate environment +source venv/bin/activate + +# Run the simulator +python run_sim.py +``` + +The simulator will: +1. Load the G1 robot model +2. Initialize DDS communication on domain 0 +3. Open a MuJoCo visualization window +4. Start listening for motor commands via DDS + +### Running with Your Robot Control Code + +Once the simulator is running, you can control it from another terminal: + +```bash +# In another terminal +cd /home/yope/Documents/lerobot +conda activate unitree_lerobot + +# Run your existing control code +python test_locomotion_minimal.py +``` + +Your `unitree_g1.py` code will automatically connect to the simulator via DDS! + +## Configuration + +Edit `config.yaml` to customize: + +- **SIMULATE_DT**: Simulation timestep (default: 0.002s = 500Hz) +- **DOMAIN_ID**: DDS domain ID (default: 0) +- **ENABLE_ONSCREEN**: Show visualization (default: true) +- **USE_JOYSTICK**: Enable gamepad control (default: false) +- **ROBOT_SCENE**: Path to MuJoCo XML scene + +### PD Gains + +The simulator uses the following PD gains (matching NVIDIA GR00T): + +**Legs (indices 0-11):** +- Hip joints: KP=150, KD=2 +- Knee joints: KP=300, KD=4 +- Ankle joints: KP=40, KD=2 + +**Waist (indices 12-14):** +- All waist joints: KP=250, KD=5 + +**Arms (indices 15-28):** +- Shoulders: KP=100, KD=2-5 +- Elbows/Wrists: KP=20-40, KD=1-2 + +## Troubleshooting + +### ImportError: cannot import name 'ChannelFactoryInitialize' + +```bash +pip install --upgrade unitree-sdk2py +``` + +### ROS2/rclpy errors + +If you don't need camera publishing, edit `sim/base_sim.py`: +- Comment out line 11: `import rclpy` +- Comment out lines 609-617 (ROS2 initialization) + +### Meshes not found + +Make sure mesh paths in `assets/g1_29dof_with_hand.xml` are relative: +```xml + +``` + +### Robot falls immediately + +Check that: +1. PD gains match NVIDIA's values (see config.yaml) +2. Velocity command scaling is correct (ang_vel_scale=0.25, cmd_scale=[2.0, 2.0, 0.25]) +3. Observations for missing joints are zeroed out (indices 12, 14, 20, 21, 27, 28) + +## Technical Details + +### Communication + +The simulator publishes: +- **`rt/lowstate`**: Robot state (joint positions, velocities, IMU, etc.) +- **`rt/wirelesscontroller`**: Wireless remote controller state (if joystick enabled) + +The simulator subscribes to: +- **`rt/lowcmd`**: Motor commands (position, velocity, torque, KP, KD) + +### Coordinate Frames + +- **World frame**: Z-up +- **Joint ordering**: 29 DOF (12 legs + 3 waist + 14 arms) +- **IMU**: Quaternion in [w, x, y, z] format + +### Performance + +- Simulation runs at ~500Hz (2ms timestep) +- Viewer updates at ~50Hz (20ms) +- Typical CPU usage: 20-40% on single core + +## Files from GR00T-WholeBodyControl + +This standalone simulator was extracted from: +- `gr00t_wbc/control/envs/g1/sim/` (simulation modules) +- `gr00t_wbc/control/robot_model/model_data/g1/` (robot model files) +- `gr00t_wbc/control/main/teleop/configs/g1_29dof_gear_wbc.yaml` (configuration) + +## License + +Follows the license of the original GR00T-WholeBodyControl repository. + diff --git a/mujoco_sim_g1/assets/meshes/head_link.STL b/mujoco_sim_g1/assets/meshes/head_link.STL new file mode 100644 index 000000000..2ee5fba15 Binary files /dev/null and b/mujoco_sim_g1/assets/meshes/head_link.STL differ diff --git a/mujoco_sim_g1/assets/meshes/left_ankle_pitch_link.STL b/mujoco_sim_g1/assets/meshes/left_ankle_pitch_link.STL new file mode 100644 index 000000000..69de84901 Binary files /dev/null and b/mujoco_sim_g1/assets/meshes/left_ankle_pitch_link.STL differ diff --git a/mujoco_sim_g1/assets/meshes/left_ankle_roll_link.STL b/mujoco_sim_g1/assets/meshes/left_ankle_roll_link.STL new file mode 100644 index 000000000..8864e9f98 Binary files /dev/null and b/mujoco_sim_g1/assets/meshes/left_ankle_roll_link.STL differ diff --git a/mujoco_sim_g1/assets/meshes/left_elbow_link.STL 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+USE_JOYSTICK: 0 # Simulate Unitree WirelessController using a gamepad (0: disable, 1: enable) +JOYSTICK_TYPE: "xbox" # support "xbox" and "switch" gamepad layout +JOYSTICK_DEVICE: 0 # Joystick number + +FREE_BASE: False +enable_waist: False # Enable waist joints control (False = band on torso, True = band on pelvis) + +PRINT_SCENE_INFORMATION: True # Print link, joint and sensors information of robot +ENABLE_ELASTIC_BAND: True # Virtual spring band, used for lifting h1 + +SIMULATE_DT: 0.002 # Simulation timestep (500Hz) +ENABLE_ONSCREEN: true # Show visualization window +ENABLE_OFFSCREEN: false # Offscreen rendering for cameras (set to true to enable camera publishing) +IMAGE_DT: 0.033333 # Image publishing rate ~30Hz +MP_START_METHOD: "spawn" # Multiprocessing start method for image subprocess +VIEWER_DT: 0.02 # Viewer update time +REWARD_DT: 0.02 +USE_SENSOR: False +USE_HISTORY: True +USE_HISTORY_LOCO: True +USE_HISTORY_MIMIC: True + +GAIT_PERIOD: 0.9 # 1.25 + +MOTOR2JOINT: [0, 1, 2, 3, 4, 5, + 6, 7, 8, 9, 10, 11, + 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28] + +JOINT2MOTOR: [0, 1, 2, 3, 4, 5, + 6, 7, 8, 9, 10, 11, + 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28] + + +UNITREE_LEGGED_CONST: + HIGHLEVEL: 0xEE + LOWLEVEL: 0xFF + TRIGERLEVEL: 0xF0 + PosStopF: 2146000000.0 + VelStopF: 16000.0 + MODE_MACHINE: 5 + MODE_PR: 0 + +JOINT_KP: [ + 100, 100, 100, 200, 20, 20, + 100, 100, 100, 200, 20, 20, + 400, 400, 400, + 90, 60, 20, 60, 4, 4, 4, + 90, 60, 20, 60, 4, 4, 4 +] + + +JOINT_KD: [ + 2.5, 2.5, 2.5, 5, 0.2, 0.1, + 2.5, 2.5, 2.5, 5, 0.2, 0.1, + 5.0, 5.0, 5.0, + 2.0, 1.0, 0.4, 1.0, 0.2, 0.2, 0.2, + 2.0, 1.0, 0.4, 1.0, 0.2, 0.2, 0.2 +] + +# arm kp +# soft kp, safe, test it first +# 50, 50, 20, 20, 10, 10, 10 +# hard kp, use only if policy is safe +# 200, 200, 80, 80, 50, 50, 50, + +# MOTOR_KP: [ +# 100, 100, 100, 200, 20, 20, +# 100, 100, 100, 200, 20, 20, +# 400, 400, 400, +# 50, 50, 20, 20, 10, 10, 10, +# 50, 50, 20, 20, 10, 10, 10 +# ] + +MOTOR_KP: [ + 150, 150, 150, 200, 40, 40, + 150, 150, 150, 200, 40, 40, + 800, 250, 800, + 100, 100, 40, 40, 100, 100, 100, + 100, 100, 40, 40, 100, 100, 100 +] + +MOTOR_KD: [ + 2, 2, 2, 4, 2, 2, + 2, 2, 2, 4, 2, 2, + 10, 5, 10, + 5, 5, 2, 2, 5, 5, 5, + 5, 5, 2, 2, 5, 5, 5 +] + +# MOTOR_KP: [ +# 100, 100, 100, 200, 20, 20, +# 100, 100, 100, 200, 20, 20, +# 400, 400, 400, +# 90, 60, 20, 60, 4, 4, 4, +# 90, 60, 20, 60, 4, 4, 4 +# ] + + +# MOTOR_KD: [ +# 2.5, 2.5, 2.5, 5, 0.2, 0.1, +# 2.5, 2.5, 2.5, 5, 0.2, 0.1, +# 5.0, 5.0, 5.0, +# 2.0, 1.0, 0.4, 1.0, 0.2, 0.2, 0.2, +# 2.0, 1.0, 0.4, 1.0, 0.2, 0.2, 0.2 +# ] + + +WeakMotorJointIndex: + left_hip_yaw_joint: 0 + left_hip_roll_joint: 1 + left_hip_pitch_joint: 2 + left_knee_joint: 3 + left_ankle_pitch_joint: 4 + left_ankle_roll_joint: 5 + right_hip_yaw_joint: 6 + right_hip_roll_joint: 7 + right_hip_pitch_joint: 8 + right_knee_joint: 9 + right_ankle_pitch_joint: 10 + right_ankle_roll_joint: 11 + waist_yaw_joint : 12 + waist_roll_joint : 13 + waist_pitch_joint : 14 + left_shoulder_pitch_joint: 15 + left_shoulder_roll_joint: 16 + left_shoulder_yaw_joint: 17 + left_elbow_joint: 18 + left_wrist_roll_joint: 19 + left_wrist_pitch_joint: 20 + left_wrist_yaw_joint: 21 + right_shoulder_pitch_joint: 22 + right_shoulder_roll_joint: 23 + right_shoulder_yaw_joint: 24 + right_elbow_joint: 25 + right_wrist_roll_joint: 26 + right_wrist_pitch_joint: 27 + right_wrist_yaw_joint: 28 + +NUM_MOTORS: 29 +NUM_JOINTS: 29 +NUM_HAND_MOTORS: 7 +NUM_HAND_JOINTS: 7 +NUM_UPPER_BODY_JOINTS: 17 + +DEFAULT_DOF_ANGLES: [ + -0.1, # left_hip_pitch_joint + 0.0, # left_hip_roll_joint + 0.0, # left_hip_yaw_joint + 0.3, # left_knee_joint + -0.2, # left_ankle_pitch_joint + 0.0, # left_ankle_roll_joint + -0.1, # right_hip_pitch_joint + 0.0, # right_hip_roll_joint + 0.0, # right_hip_yaw_joint + 0.3, # right_knee_joint + -0.2, # right_ankle_pitch_joint + 0.0, # right_ankle_roll_joint + 0.0, # waist_yaw_joint + 0.0, # waist_roll_joint + 0.0, # waist_pitch_joint + 0.0, # left_shoulder_pitch_joint + 0.0, # left_shoulder_roll_joint + 0.0, # left_shoulder_yaw_joint + 0.0, # left_elbow_joint + 0.0, # left_wrist_roll_joint + 0.0, # left_wrist_pitch_joint + 0.0, # left_wrist_yaw_joint + 0.0, # right_shoulder_pitch_joint + 0.0, # right_shoulder_roll_joint + 0.0, # right_shoulder_yaw_joint + 0.0, # right_elbow_joint + 0.0, # right_wrist_roll_joint + 0.0, # right_wrist_pitch_joint + 0.0 # right_wrist_yaw_joint +] + +DEFAULT_MOTOR_ANGLES: [ + -0.1, # left_hip_pitch_joint + 0.0, # left_hip_roll_joint + 0.0, # left_hip_yaw_joint + 0.3, # left_knee_joint + -0.2, # left_ankle_pitch_joint + 0.0, # left_ankle_roll_joint + -0.1, # right_hip_pitch_joint + 0.0, # right_hip_roll_joint + 0.0, # right_hip_yaw_joint + 0.3, # right_knee_joint + -0.2, # right_ankle_pitch_joint + 0.0, # right_ankle_roll_joint + 0.0, # waist_yaw_joint + 0.0, # waist_roll_joint + 0.0, # waist_pitch_joint + 0.0, # left_shoulder_pitch_joint + 0.0, # left_shoulder_roll_joint + 0.0, # left_shoulder_yaw_joint + 0.0, # left_elbow_joint + 0.0, # left_wrist_roll_joint + 0.0, # left_wrist_pitch_joint + 0.0, # left_wrist_yaw_joint + 0.0, # right_shoulder_pitch_joint + 0.0, # right_shoulder_roll_joint + 0.0, # right_shoulder_yaw_joint + 0.0, # right_elbow_joint + 0.0, # right_wrist_roll_joint + 0.0, # right_wrist_pitch_joint + 0.0 # right_wrist_yaw_joint +] + +motor_pos_lower_limit_list: [-2.5307, -0.5236, -2.7576, -0.087267, -0.87267, -0.2618, + -2.5307, -2.9671, -2.7576, -0.087267, -0.87267, -0.2618, + -2.618, -0.52, -0.52, + -3.0892, -1.5882, -2.618, -1.0472, + -1.972222054, -1.61443, -1.61443, + -3.0892, -2.2515, -2.618, -1.0472, + -1.972222054, -1.61443, -1.61443] +motor_pos_upper_limit_list: [2.8798, 2.9671, 2.7576, 2.8798, 0.5236, 0.2618, + 2.8798, 0.5236, 2.7576, 2.8798, 0.5236, 0.2618, + 2.618, 0.52, 0.52, + 2.6704, 2.2515, 2.618, 2.0944, + 1.972222054, 1.61443, 1.61443, + 2.6704, 1.5882, 2.618, 2.0944, + 1.972222054, 1.61443, 1.61443] +motor_vel_limit_list: [32.0, 32.0, 32.0, 20.0, 37.0, 37.0, + 32.0, 32.0, 32.0, 20.0, 37.0, 37.0, + 32.0, 37.0, 37.0, + 37.0, 37.0, 37.0, 37.0, + 37.0, 22.0, 22.0, + 37.0, 37.0, 37.0, 37.0, + 37.0, 22.0, 22.0] +motor_effort_limit_list: [88.0, 88.0, 88.0, 139.0, 50.0, 50.0, + 88.0, 88.0, 88.0, 139.0, 50.0, 50.0, + 88.0, 50.0, 50.0, + 25.0, 25.0, 25.0, 25.0, + 25.0, 5.0, 5.0, + 2.45, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, + 25.0, 25.0, 25.0, 25.0, + 25.0, 5.0, 5.0, + 2.45, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7] +history_config: { + base_ang_vel: 4, + projected_gravity: 4, + command_lin_vel: 4, + command_ang_vel: 4, + command_base_height: 4, + command_stand: 4, + ref_upper_dof_pos: 4, + dof_pos: 4, + dof_vel: 4, + actions: 4, + # phase_time: 4, + ref_motion_phase: 4, + sin_phase: 4, + cos_phase: 4 + } +history_loco_config: { + base_ang_vel: 4, + projected_gravity: 4, + command_lin_vel: 4, + command_ang_vel: 4, + # command_base_height: 4, + command_stand: 4, + ref_upper_dof_pos: 4, + dof_pos: 4, + dof_vel: 4, + actions: 4, + # phase_time: 4, + sin_phase: 4, + cos_phase: 4 + } +history_loco_height_config: { + base_ang_vel: 4, + projected_gravity: 4, + command_lin_vel: 4, + command_ang_vel: 4, + command_base_height: 4, + command_stand: 4, + ref_upper_dof_pos: 4, + dof_pos: 4, + dof_vel: 4, + actions: 4, + # phase_time: 4, + sin_phase: 4, + cos_phase: 4 + } +history_mimic_config: { + base_ang_vel: 4, + projected_gravity: 4, + dof_pos: 4, + dof_vel: 4, + actions: 4, + ref_motion_phase: 4, + } +obs_dims: { + base_lin_vel: 3, + base_ang_vel: 3, + projected_gravity: 3, + command_lin_vel: 2, + command_ang_vel: 1, + command_stand: 1, + command_base_height: 1, + ref_upper_dof_pos: 17, # upper body actions + dof_pos: 29, + dof_vel: 29, + # actions: 12, # lower body actions + actions: 29, # full body actions + phase_time: 1, + ref_motion_phase: 1, # mimic motion phase + sin_phase: 1, + cos_phase: 1, + } +obs_loco_dims: { + base_lin_vel: 3, + base_ang_vel: 3, + projected_gravity: 3, + command_lin_vel: 2, + command_ang_vel: 1, + command_stand: 1, + command_base_height: 1, + ref_upper_dof_pos: 17, # upper body actions + dof_pos: 29, + dof_vel: 29, + actions: 12, # lower body actions + phase_time: 1, + sin_phase: 1, + cos_phase: 1, + } +obs_mimic_dims: { + base_lin_vel: 3, + base_ang_vel: 3, + projected_gravity: 3, + dof_pos: 29, + dof_vel: 29, + actions: 29, # full body actions + ref_motion_phase: 1, # mimic motion phase + } +obs_scales: { + base_lin_vel: 2.0, + base_ang_vel: 0.25, + projected_gravity: 1.0, + command_lin_vel: 1, + command_ang_vel: 1, + command_stand: 1, + command_base_height: 2, # Yuanhang: it's 2, not 1! + ref_upper_dof_pos: 1.0, + dof_pos: 1.0, + dof_vel: 0.05, + history: 1.0, + history_loco: 1.0, + history_mimic: 1.0, + actions: 1.0, + phase_time: 1.0, + ref_motion_phase: 1.0, + sin_phase: 1.0, + cos_phase: 1.0 + } + +loco_upper_body_dof_pos: [ + 0.0, 0.0, 0.0, # waist + 0.0, 0.3, 0.0, 1.0, # left shoulder and elbow + 0.0, 0.0, 0.0, # left wrist + 0.0, -0.3, 0.0, 1.0, # right shoulder and elbow + 0.0, 0.0, 0.0 # right wrist +] + +robot_dofs: { + "g1_29dof": [1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, + 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1], + "g1_29dof_anneal_23dof": [1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, + 1, 1, 1, + 1, 1, 1, 1, 0, 0, 0, + 1, 1, 1, 1, 0, 0, 0], +} + +mimic_robot_types: { + + "APT_level1": "g1_29dof_anneal_23dof", +} + + + + +# 01281657 +mimic_models: { + "APT_level1": "20250116_225127-TairanTestbed_G129dofANNEAL23dof_dm_APT_video_APT_level1_MinimalFriction-0.3_RfiTrue_Far0.325_RESUME_LARGENOISE-motion_tracking-g1_29dof_anneal_23dof/exported/model_176500.onnx", + +} + + + +start_upper_body_dof_pos: { + + "APT_level1": + [0.19964170455932617, 0.07710712403059006, -0.2882401943206787, + 0.21672365069389343, 0.15629297494888306, -0.5167576670646667, 0.5782126784324646, + 0.0, 0.0, 0.0, + 0.25740593671798706, -0.2504104673862457, 0.22500675916671753, 0.5127624273300171, + 0.0, 0.0, 0.0], + +} + +motion_length_s: { + "APT_level1": 7.66, + +} diff --git a/mujoco_sim_g1/requirements.txt b/mujoco_sim_g1/requirements.txt new file mode 100644 index 000000000..32e4c434a --- /dev/null +++ b/mujoco_sim_g1/requirements.txt @@ -0,0 +1,13 @@ +mujoco>=3.0.0 +numpy>=1.24.0 +pyyaml>=6.0 +unitree-sdk2py>=1.0.0 +loguru>=0.7.0 + +# Camera publishing dependencies +opencv-python>=4.8.0 +pyzmq>=25.0.0 +msgpack>=1.0.0 +msgpack-numpy>=0.4.8 +matplotlib>=3.5.0 # For live camera viewer + diff --git a/mujoco_sim_g1/run_sim.py b/mujoco_sim_g1/run_sim.py new file mode 100755 index 000000000..7b86b86e9 --- /dev/null +++ b/mujoco_sim_g1/run_sim.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +"""Standalone MuJoCo simulator for Unitree G1""" +import argparse +import sys +from pathlib import Path + +# Add sim module to path +sys.path.insert(0, str(Path(__file__).parent)) + +import yaml +from sim.simulator_factory import SimulatorFactory, init_channel + +def main(): + parser = argparse.ArgumentParser(description="Unitree G1 MuJoCo Simulator") + parser.add_argument("--publish-images", action="store_true", + help="Enable camera image publishing via ZMQ (requires offscreen rendering)") + parser.add_argument("--camera-port", type=int, default=5555, + help="ZMQ port for camera publishing (default: 5555)") + parser.add_argument("--cameras", type=str, nargs="+", default=None, + help="Camera names to publish (default: head_camera)") + args = parser.parse_args() + + # Load config + config_path = Path(__file__).parent / "config.yaml" + with open(config_path) as f: + config = yaml.safe_load(f) + + # Override config with command line args + enable_offscreen = args.publish_images or config.get("ENABLE_OFFSCREEN", False) + + print("="*60) + print("🤖 Starting Unitree G1 MuJoCo Simulator") + print("="*60) + print(f"📁 Scene: {config['ROBOT_SCENE']}") + print(f"⏱️ Timestep: {config['SIMULATE_DT']}s ({int(1/config['SIMULATE_DT'])} Hz)") + print(f"👁️ Visualization: {'ON' if config.get('ENABLE_ONSCREEN', True) else 'OFF'}") + + # Configure cameras if requested + camera_configs = {} + if enable_offscreen: + camera_list = args.cameras or ["head_camera"] + for cam_name in camera_list: + camera_configs[cam_name] = {"height": 480, "width": 640} + print(f"📷 Cameras: {', '.join(camera_list)} → ZMQ port {args.camera_port}") + + print("="*60) + + # Initialize DDS channel + init_channel(config=config) + + # Create simulator + sim = SimulatorFactory.create_simulator( + config=config, + env_name="default", + onscreen=config.get("ENABLE_ONSCREEN", True), + offscreen=enable_offscreen, + camera_configs=camera_configs, + ) + + # Start simulator (blocking) + print("\nSimulator running. Press Ctrl+C to exit.") + if enable_offscreen and args.publish_images: + print(f"Camera images publishing on tcp://localhost:{args.camera_port}") + try: + SimulatorFactory.start_simulator( + sim, + as_thread=False, + enable_image_publish=args.publish_images, + camera_port=args.camera_port, + ) + except KeyboardInterrupt: + print("\nShutting down simulator...") + sim.close() + +if __name__ == "__main__": + main() + diff --git a/mujoco_sim_g1/sim/__init__.py b/mujoco_sim_g1/sim/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/mujoco_sim_g1/sim/base_sim.py b/mujoco_sim_g1/sim/base_sim.py new file mode 100644 index 000000000..707718bfc --- /dev/null +++ b/mujoco_sim_g1/sim/base_sim.py @@ -0,0 +1,803 @@ +import argparse +import pathlib +from pathlib import Path +import threading +from threading import Lock, Thread +from typing import Dict + +import mujoco +import mujoco.viewer +import numpy as np +try: + import rclpy + HAS_RCLPY = True +except ImportError: + HAS_RCLPY = False + print("Warning: rclpy not found. Camera image publishing will be disabled.") +from unitree_sdk2py.core.channel import ChannelFactoryInitialize +import yaml + +from .image_publish_utils import ImagePublishProcess +from .metric_utils import check_contact, check_height +from .sim_utilts import get_subtree_body_names +from .unitree_sdk2py_bridge import ElasticBand, UnitreeSdk2Bridge + +GR00T_WBC_ROOT = Path(__file__).resolve().parent.parent # Points to mujoco_sim_g1/ + + +class DefaultEnv: + """Base environment class that handles simulation environment setup and step""" + + def __init__( + self, + config: Dict[str, any], + env_name: str = "default", + camera_configs: Dict[str, any] = None, + onscreen: bool = False, + offscreen: bool = False, + ): + # Avoid mutable default argument gotcha + if camera_configs is None: + camera_configs = {} + + # global_view is only set up for this specifc scene for now. + if config["ROBOT_SCENE"] == "gr00t_wbc/control/robot_model/model_data/g1/scene_29dof.xml": + camera_configs["global_view"] = { + "height": 400, + "width": 400, + } + self.config = config + self.env_name = env_name + self.num_body_dof = self.config["NUM_JOINTS"] + self.num_hand_dof = self.config["NUM_HAND_JOINTS"] + self.sim_dt = self.config["SIMULATE_DT"] + self.obs = None + self.torques = np.zeros(self.num_body_dof + self.num_hand_dof * 2) + self.torque_limit = np.array(self.config["motor_effort_limit_list"]) + self.camera_configs = camera_configs + + # Debug: print camera config + if len(camera_configs) > 0: + print(f"✓ DefaultEnv initialized with {len(camera_configs)} camera(s): {list(camera_configs.keys())}") + + # Thread safety lock + self.reward_lock = Lock() + + # Unitree bridge will be initialized by the simulator + self.unitree_bridge = None + + # Store display mode + self.onscreen = onscreen + + # Initialize scene (defined in subclasses) + self.init_scene() + self.last_reward = 0 + + # Setup offscreen rendering if needed + self.offscreen = offscreen + if self.offscreen: + self.init_renderers() + self.image_dt = self.config.get("IMAGE_DT", 0.033333) + + # Image publishing subprocess (initialized separately) + self.image_publish_process = None + + def init_scene(self): + """Initialize the default robot scene""" + self.mj_model = mujoco.MjModel.from_xml_path( + str(pathlib.Path(GR00T_WBC_ROOT) / self.config["ROBOT_SCENE"]) + ) + self.mj_data = mujoco.MjData(self.mj_model) + self.mj_model.opt.timestep = self.sim_dt + self.torso_index = mujoco.mj_name2id(self.mj_model, mujoco.mjtObj.mjOBJ_BODY, "torso_link") + self.root_body = "pelvis" + # Enable the elastic band + if self.config["ENABLE_ELASTIC_BAND"]: + self.elastic_band = ElasticBand() + if "g1" in self.config["ROBOT_TYPE"]: + if self.config["enable_waist"]: + self.band_attached_link = self.mj_model.body("pelvis").id + else: + self.band_attached_link = self.mj_model.body("torso_link").id + elif "h1" in self.config["ROBOT_TYPE"]: + self.band_attached_link = self.mj_model.body("torso_link").id + else: + self.band_attached_link = self.mj_model.body("base_link").id + + if self.onscreen: + self.viewer = mujoco.viewer.launch_passive( + self.mj_model, + self.mj_data, + key_callback=self.elastic_band.MujuocoKeyCallback, + show_left_ui=False, + show_right_ui=False, + ) + else: + mujoco.mj_forward(self.mj_model, self.mj_data) + self.viewer = None + else: + if self.onscreen: + self.viewer = mujoco.viewer.launch_passive( + self.mj_model, self.mj_data, show_left_ui=False, show_right_ui=False + ) + else: + mujoco.mj_forward(self.mj_model, self.mj_data) + self.viewer = None + + if self.viewer: + # viewer camera + self.viewer.cam.azimuth = 120 # Horizontal rotation in degrees + self.viewer.cam.elevation = -30 # Vertical tilt in degrees + self.viewer.cam.distance = 2.0 # Distance from camera to target + self.viewer.cam.lookat = np.array([0, 0, 0.5]) # Point the camera is looking at + + # Note that the actuator order is the same as the joint order in the mujoco model. + self.body_joint_index = [] + self.left_hand_index = [] + self.right_hand_index = [] + for i in range(self.mj_model.njnt): + name = self.mj_model.joint(i).name + if any( + [ + part_name in name + for part_name in ["hip", "knee", "ankle", "waist", "shoulder", "elbow", "wrist"] + ] + ): + self.body_joint_index.append(i) + elif "left_hand" in name: + self.left_hand_index.append(i) + elif "right_hand" in name: + self.right_hand_index.append(i) + + assert len(self.body_joint_index) == self.config["NUM_JOINTS"], \ + f"Expected {self.config['NUM_JOINTS']} body joints, got {len(self.body_joint_index)}" + # Hand joints are optional (some models don't have hands) + if self.config.get("NUM_HAND_JOINTS", 0) > 0: + expected_hands = self.config["NUM_HAND_JOINTS"] + if len(self.left_hand_index) != expected_hands or len(self.right_hand_index) != expected_hands: + print(f"Warning: Expected {expected_hands} hand joints, got left={len(self.left_hand_index)}, right={len(self.right_hand_index)}") + print("Continuing without hands...") + + self.body_joint_index = np.array(self.body_joint_index) + self.left_hand_index = np.array(self.left_hand_index) + self.right_hand_index = np.array(self.right_hand_index) + + def init_renderers(self): + # Initialize camera renderers + self.renderers = {} + for camera_name, camera_config in self.camera_configs.items(): + renderer = mujoco.Renderer( + self.mj_model, height=camera_config["height"], width=camera_config["width"] + ) + self.renderers[camera_name] = renderer + + def start_image_publish_subprocess(self, start_method: str = "spawn", camera_port: int = 5555): + """Start image publishing subprocess using ZMQ""" + # Use spawn method for better GIL isolation, or configured method + if len(self.camera_configs) == 0: + print( + "Warning: No camera configs provided, image publishing subprocess will not be started" + ) + return + start_method = self.config.get("MP_START_METHOD", "spawn") + self.image_publish_process = ImagePublishProcess( + camera_configs=self.camera_configs, + image_dt=self.image_dt, + zmq_port=camera_port, + start_method=start_method, + verbose=self.config.get("verbose", False), + ) + self.image_publish_process.start_process() + print(f"✓ Started image publishing subprocess on ZMQ port {camera_port}") + + def compute_body_torques(self) -> np.ndarray: + """Compute body torques based on the current robot state""" + body_torques = np.zeros(self.num_body_dof) + if self.unitree_bridge is not None and self.unitree_bridge.low_cmd: + for i in range(self.unitree_bridge.num_body_motor): + if self.unitree_bridge.use_sensor: + body_torques[i] = ( + self.unitree_bridge.low_cmd.motor_cmd[i].tau + + self.unitree_bridge.low_cmd.motor_cmd[i].kp + * (self.unitree_bridge.low_cmd.motor_cmd[i].q - self.mj_data.sensordata[i]) + + self.unitree_bridge.low_cmd.motor_cmd[i].kd + * ( + self.unitree_bridge.low_cmd.motor_cmd[i].dq + - self.mj_data.sensordata[i + self.unitree_bridge.num_body_motor] + ) + ) + else: + body_torques[i] = ( + self.unitree_bridge.low_cmd.motor_cmd[i].tau + + self.unitree_bridge.low_cmd.motor_cmd[i].kp + * ( + self.unitree_bridge.low_cmd.motor_cmd[i].q + - self.mj_data.qpos[self.body_joint_index[i] + 7 - 1] + ) + + self.unitree_bridge.low_cmd.motor_cmd[i].kd + * ( + self.unitree_bridge.low_cmd.motor_cmd[i].dq + - self.mj_data.qvel[self.body_joint_index[i] + 6 - 1] + ) + ) + return body_torques + + def compute_hand_torques(self) -> np.ndarray: + """Compute hand torques based on the current robot state""" + left_hand_torques = np.zeros(self.num_hand_dof) + right_hand_torques = np.zeros(self.num_hand_dof) + if self.unitree_bridge is not None and self.unitree_bridge.low_cmd: + for i in range(self.unitree_bridge.num_hand_motor): + left_hand_torques[i] = ( + self.unitree_bridge.left_hand_cmd.motor_cmd[i].tau + + self.unitree_bridge.left_hand_cmd.motor_cmd[i].kp + * ( + self.unitree_bridge.left_hand_cmd.motor_cmd[i].q + - self.mj_data.qpos[self.left_hand_index[i] + 7 - 1] + ) + + self.unitree_bridge.left_hand_cmd.motor_cmd[i].kd + * ( + self.unitree_bridge.left_hand_cmd.motor_cmd[i].dq + - self.mj_data.qvel[self.left_hand_index[i] + 6 - 1] + ) + ) + right_hand_torques[i] = ( + self.unitree_bridge.right_hand_cmd.motor_cmd[i].tau + + self.unitree_bridge.right_hand_cmd.motor_cmd[i].kp + * ( + self.unitree_bridge.right_hand_cmd.motor_cmd[i].q + - self.mj_data.qpos[self.right_hand_index[i] + 7 - 1] + ) + + self.unitree_bridge.right_hand_cmd.motor_cmd[i].kd + * ( + self.unitree_bridge.right_hand_cmd.motor_cmd[i].dq + - self.mj_data.qvel[self.right_hand_index[i] + 6 - 1] + ) + ) + return np.concatenate((left_hand_torques, right_hand_torques)) + + def compute_body_qpos(self) -> np.ndarray: + """Compute body joint positions based on the current command""" + body_qpos = np.zeros(self.num_body_dof) + if self.unitree_bridge is not None and self.unitree_bridge.low_cmd: + for i in range(self.unitree_bridge.num_body_motor): + body_qpos[i] = self.unitree_bridge.low_cmd.motor_cmd[i].q + return body_qpos + + def compute_hand_qpos(self) -> np.ndarray: + """Compute hand joint positions based on the current command""" + hand_qpos = np.zeros(self.num_hand_dof * 2) + if self.unitree_bridge is not None and self.unitree_bridge.low_cmd: + for i in range(self.unitree_bridge.num_hand_motor): + hand_qpos[i] = self.unitree_bridge.left_hand_cmd.motor_cmd[i].q + hand_qpos[i + self.num_hand_dof] = self.unitree_bridge.right_hand_cmd.motor_cmd[i].q + return hand_qpos + + def prepare_obs(self) -> Dict[str, any]: + """Prepare observation dictionary from the current robot state""" + obs = {} + obs["floating_base_pose"] = self.mj_data.qpos[:7] + obs["floating_base_vel"] = self.mj_data.qvel[:6] + obs["floating_base_acc"] = self.mj_data.qacc[:6] + obs["secondary_imu_quat"] = self.mj_data.xquat[self.torso_index] + obs["secondary_imu_vel"] = self.mj_data.cvel[self.torso_index] + obs["body_q"] = self.mj_data.qpos[self.body_joint_index + 7 - 1] + obs["body_dq"] = self.mj_data.qvel[self.body_joint_index + 6 - 1] + obs["body_ddq"] = self.mj_data.qacc[self.body_joint_index + 6 - 1] + obs["body_tau_est"] = self.mj_data.actuator_force[self.body_joint_index - 1] + if self.num_hand_dof > 0: + obs["left_hand_q"] = self.mj_data.qpos[self.left_hand_index + 7 - 1] + obs["left_hand_dq"] = self.mj_data.qvel[self.left_hand_index + 6 - 1] + obs["left_hand_ddq"] = self.mj_data.qacc[self.left_hand_index + 6 - 1] + obs["left_hand_tau_est"] = self.mj_data.actuator_force[self.left_hand_index - 1] + obs["right_hand_q"] = self.mj_data.qpos[self.right_hand_index + 7 - 1] + obs["right_hand_dq"] = self.mj_data.qvel[self.right_hand_index + 6 - 1] + obs["right_hand_ddq"] = self.mj_data.qacc[self.right_hand_index + 6 - 1] + obs["right_hand_tau_est"] = self.mj_data.actuator_force[self.right_hand_index - 1] + obs["time"] = self.mj_data.time + return obs + + def sim_step(self): + self.obs = self.prepare_obs() + self.unitree_bridge.PublishLowState(self.obs) + if self.unitree_bridge.joystick: + self.unitree_bridge.PublishWirelessController() + if self.config["ENABLE_ELASTIC_BAND"]: + if self.elastic_band.enable: + # Get Cartesian pose and velocity of the band_attached_link + pose = np.concatenate( + [ + self.mj_data.xpos[self.band_attached_link], # link position in world + self.mj_data.xquat[ + self.band_attached_link + ], # link quaternion in world [w,x,y,z] + np.zeros(6), # placeholder for velocity + ] + ) + + # Get velocity in world frame + mujoco.mj_objectVelocity( + self.mj_model, + self.mj_data, + mujoco.mjtObj.mjOBJ_BODY, + self.band_attached_link, + pose[7:13], + 0, # 0 for world frame + ) + + # Reorder velocity from [ang, lin] to [lin, ang] + pose[7:10], pose[10:13] = pose[10:13], pose[7:10].copy() + self.mj_data.xfrc_applied[self.band_attached_link] = self.elastic_band.Advance(pose) + else: + # explicitly resetting the force when the band is not enabled + self.mj_data.xfrc_applied[self.band_attached_link] = np.zeros(6) + body_torques = self.compute_body_torques() + hand_torques = self.compute_hand_torques() + self.torques[self.body_joint_index - 1] = body_torques + if self.num_hand_dof > 0: + self.torques[self.left_hand_index - 1] = hand_torques[: self.num_hand_dof] + self.torques[self.right_hand_index - 1] = hand_torques[self.num_hand_dof :] + + self.torques = np.clip(self.torques, -self.torque_limit, self.torque_limit) + + if self.config["FREE_BASE"]: + self.mj_data.ctrl = np.concatenate((np.zeros(6), self.torques)) + else: + self.mj_data.ctrl = self.torques + mujoco.mj_step(self.mj_model, self.mj_data) + # self.check_self_collision() + + def kinematics_step(self): + """ + Run kinematics only: compute the qpos of the robot and directly set the qpos. + For debugging purposes. + """ + if self.unitree_bridge is not None: + self.unitree_bridge.PublishLowState(self.prepare_obs()) + if self.unitree_bridge.joystick: + self.unitree_bridge.PublishWirelessController() + + if self.config["ENABLE_ELASTIC_BAND"]: + if self.elastic_band.enable: + # Get Cartesian pose and velocity of the band_attached_link + pose = np.concatenate( + [ + self.mj_data.xpos[self.band_attached_link], # link position in world + self.mj_data.xquat[ + self.band_attached_link + ], # link quaternion in world [w,x,y,z] + np.zeros(6), # placeholder for velocity + ] + ) + + # Get velocity in world frame + mujoco.mj_objectVelocity( + self.mj_model, + self.mj_data, + mujoco.mjtObj.mjOBJ_BODY, + self.band_attached_link, + pose[7:13], + 0, # 0 for world frame + ) + + # Reorder velocity from [ang, lin] to [lin, ang] + pose[7:10], pose[10:13] = pose[10:13], pose[7:10].copy() + + self.mj_data.xfrc_applied[self.band_attached_link] = self.elastic_band.Advance(pose) + else: + # explicitly resetting the force when the band is not enabled + self.mj_data.xfrc_applied[self.band_attached_link] = np.zeros(6) + + body_qpos = self.compute_body_qpos() # (num_body_dof,) + hand_qpos = self.compute_hand_qpos() # (num_hand_dof * 2,) + + self.mj_data.qpos[self.body_joint_index + 7 - 1] = body_qpos + self.mj_data.qpos[self.left_hand_index + 7 - 1] = hand_qpos[: self.num_hand_dof] + self.mj_data.qpos[self.right_hand_index + 7 - 1] = hand_qpos[self.num_hand_dof :] + + mujoco.mj_kinematics(self.mj_model, self.mj_data) + mujoco.mj_comPos(self.mj_model, self.mj_data) + + def apply_perturbation(self, key): + """Apply perturbation to the robot""" + # Add velocity perturbations in body frame + perturbation_x_body = 0.0 # forward/backward in body frame + perturbation_y_body = 0.0 # left/right in body frame + if key == "up": + perturbation_x_body = 1.0 # forward + elif key == "down": + perturbation_x_body = -1.0 # backward + elif key == "left": + perturbation_y_body = 1.0 # left + elif key == "right": + perturbation_y_body = -1.0 # right + + # Transform body frame velocity to world frame using MuJoCo's rotation + vel_body = np.array([perturbation_x_body, perturbation_y_body, 0.0]) + vel_world = np.zeros(3) + base_quat = self.mj_data.qpos[3:7] # [w, x, y, z] quaternion + + # Use MuJoCo's robust quaternion rotation (handles invalid quaternions automatically) + mujoco.mju_rotVecQuat(vel_world, vel_body, base_quat) + + # Apply to base linear velocity in world frame + self.mj_data.qvel[0] += vel_world[0] # world X velocity + self.mj_data.qvel[1] += vel_world[1] # world Y velocity + + # Update dynamics after velocity change + mujoco.mj_forward(self.mj_model, self.mj_data) + + def update_viewer(self): + if self.viewer is not None: + self.viewer.sync() + + def update_viewer_camera(self): + if self.viewer is not None: + if self.viewer.cam.type == mujoco.mjtCamera.mjCAMERA_TRACKING: + self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_FREE + else: + self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING + + def update_reward(self): + """Calculate reward. Should be implemented by subclasses.""" + with self.reward_lock: + self.last_reward = 0 + + def get_reward(self): + """Thread-safe way to get the last calculated reward.""" + with self.reward_lock: + return self.last_reward + + def set_unitree_bridge(self, unitree_bridge): + """Set the unitree bridge from the simulator""" + self.unitree_bridge = unitree_bridge + + def get_privileged_obs(self): + """Get privileged observation. Should be implemented by subclasses.""" + return {} + + def update_render_caches(self): + """Update render cache and shared memory for subprocess.""" + render_caches = {} + for camera_name, camera_config in self.camera_configs.items(): + renderer = self.renderers[camera_name] + if "params" in camera_config: + renderer.update_scene(self.mj_data, camera=camera_config["params"]) + else: + renderer.update_scene(self.mj_data, camera=camera_name) + render_caches[camera_name + "_image"] = renderer.render() + + # Update shared memory if image publishing process is available + if self.image_publish_process is not None: + self.image_publish_process.update_shared_memory(render_caches) + + return render_caches + + def handle_keyboard_button(self, key): + if self.elastic_band is not None: + self.elastic_band.handle_keyboard_button(key) + + if key == "backspace": + self.reset() + if key == "v": + self.update_viewer_camera() + if key in ["up", "down", "left", "right"]: + self.apply_perturbation(key) + + def check_fall(self): + """Check if the robot has fallen""" + self.fall = False + if self.mj_data.qpos[2] < 0.2: + self.fall = True + print(f"Warning: Robot has fallen, height: {self.mj_data.qpos[2]:.3f} m") + + if self.fall: + self.reset() + + def check_self_collision(self): + """Check for self-collision of the robot""" + robot_bodies = get_subtree_body_names(self.mj_model, self.mj_model.body(self.root_body).id) + self_collision, contact_bodies = check_contact( + self.mj_model, self.mj_data, robot_bodies, robot_bodies, return_all_contact_bodies=True + ) + if self_collision: + print(f"Warning: Self-collision detected: {contact_bodies}") + return self_collision + + def reset(self): + mujoco.mj_resetData(self.mj_model, self.mj_data) + + +class CubeEnv(DefaultEnv): + """Environment with a cube object for pick and place tasks""" + + def __init__( + self, + config: Dict[str, any], + onscreen: bool = False, + offscreen: bool = False, + ): + # Override the robot scene + config = config.copy() # Create a copy to avoid modifying the original + config["ROBOT_SCENE"] = "gr00t_wbc/control/robot_model/model_data/g1/pnp_cube_43dof.xml" + super().__init__(config, "cube", {}, onscreen, offscreen) + + def update_reward(self): + """Calculate reward based on gripper contact with cube and cube height""" + right_hand_body = [ + "right_hand_thumb_2_link", + "right_hand_middle_1_link", + "right_hand_index_1_link", + ] + gripper_cube_contact = check_contact( + self.mj_model, self.mj_data, right_hand_body, "cube_body" + ) + cube_lifted = check_height(self.mj_model, self.mj_data, "cube", 0.85, 2.0) + + with self.reward_lock: + self.last_reward = gripper_cube_contact & cube_lifted + + +class BoxEnv(DefaultEnv): + """Environment with a box object for manipulation tasks""" + + def __init__( + self, + config: Dict[str, any], + onscreen: bool = False, + offscreen: bool = False, + ): + # Override the robot scene + config = config.copy() # Create a copy to avoid modifying the original + config["ROBOT_SCENE"] = "gr00t_wbc/control/robot_model/model_data/g1/lift_box_43dof.xml" + super().__init__(config, "box", {}, onscreen, offscreen) + + def reward(self): + """Calculate reward based on gripper contact with cube and cube height""" + left_hand_body = [ + "left_hand_thumb_2_link", + "left_hand_middle_1_link", + "left_hand_index_1_link", + ] + right_hand_body = [ + "right_hand_thumb_2_link", + "right_hand_middle_1_link", + "right_hand_index_1_link", + ] + gripper_box_contact = check_contact(self.mj_model, self.mj_data, left_hand_body, "box_body") + gripper_box_contact &= check_contact( + self.mj_model, self.mj_data, right_hand_body, "box_body" + ) + box_lifted = check_height(self.mj_model, self.mj_data, "box", 0.92, 2.0) + + print("gripper_box_contact: ", gripper_box_contact, "box_lifted: ", box_lifted) + + with self.reward_lock: + self.last_reward = gripper_box_contact & box_lifted + return self.last_reward + + +class BottleEnv(DefaultEnv): + """Environment with a cylinder object for manipulation tasks""" + + def __init__( + self, + config: Dict[str, any], + onscreen: bool = False, + offscreen: bool = False, + ): + # Override the robot scene + config = config.copy() # Create a copy to avoid modifying the original + config["ROBOT_SCENE"] = "gr00t_wbc/control/robot_model/model_data/g1/pnp_bottle_43dof.xml" + camera_configs = { + "egoview": { + "height": 400, + "width": 400, + }, + } + super().__init__( + config, "cylinder", camera_configs, onscreen, offscreen + ) + + self.bottle_body = self.mj_model.body("bottle_body") + self.bottle_geom = self.mj_model.geom("bottle") + + if self.viewer is not None: + self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_FIXED + self.viewer.cam.fixedcamid = self.mj_model.camera("egoview").id + + def update_reward(self): + """Calculate reward based on gripper contact with cylinder and cylinder height""" + pass + + def get_privileged_obs(self): + obs_pos = self.mj_data.xpos[self.bottle_body.id] + obs_quat = self.mj_data.xquat[self.bottle_body.id] + return {"bottle_pos": obs_pos, "bottle_quat": obs_quat} + + +class BaseSimulator: + """Base simulator class that handles initialization and running of simulations""" + + def __init__(self, config: Dict[str, any], env_name: str = "default", **kwargs): + self.config = config + self.env_name = env_name + + # Initialize ROS 2 node (optional, only if rclpy is available) + if HAS_RCLPY: + if not rclpy.ok(): + rclpy.init() + self.node = rclpy.create_node("sim_mujoco") + self.thread = threading.Thread(target=rclpy.spin, args=(self.node,), daemon=True) + self.thread.start() + else: + self.thread = None + executor = rclpy.get_global_executor() + self.node = executor.get_nodes()[0] # will only take the first node + else: + self.node = None + self.thread = None + + # Set update frequencies + self.sim_dt = self.config["SIMULATE_DT"] + self.reward_dt = self.config.get("REWARD_DT", 0.02) + self.image_dt = self.config.get("IMAGE_DT", 0.033333) + self.viewer_dt = self.config.get("VIEWER_DT", 0.02) + + # Create the appropriate environment based on name + if env_name == "default": + self.sim_env = DefaultEnv(config, env_name, **kwargs) + elif env_name == "pnp_cube": + self.sim_env = CubeEnv(config, **kwargs) + elif env_name == "lift_box": + self.sim_env = BoxEnv(config, **kwargs) + elif env_name == "pnp_bottle": + self.sim_env = BottleEnv(config, **kwargs) + else: + raise ValueError(f"Invalid environment name: {env_name}") + + # Initialize the DDS communication layer - should be safe to call multiple times + + try: + if self.config.get("INTERFACE", None): + ChannelFactoryInitialize(self.config["DOMAIN_ID"], self.config["INTERFACE"]) + else: + ChannelFactoryInitialize(self.config["DOMAIN_ID"]) + except Exception as e: + # If it fails because it's already initialized, that's okay + print(f"Note: Channel factory initialization attempt: {e}") + + # Initialize the unitree bridge and pass it to the environment + self.init_unitree_bridge() + self.sim_env.set_unitree_bridge(self.unitree_bridge) + + # Initialize additional components + self.init_subscriber() + self.init_publisher() + + self.sim_thread = None + + def start_as_thread(self): + # Create simulation thread + self.sim_thread = Thread(target=self.start) + self.sim_thread.start() + + def start_image_publish_subprocess(self, start_method: str = "spawn", camera_port: int = 5555): + """Start the image publish subprocess""" + self.sim_env.start_image_publish_subprocess(start_method, camera_port) + + def init_subscriber(self): + """Initialize subscribers. Can be overridden by subclasses.""" + pass + + def init_publisher(self): + """Initialize publishers. Can be overridden by subclasses.""" + pass + + def init_unitree_bridge(self): + """Initialize the unitree SDK bridge""" + self.unitree_bridge = UnitreeSdk2Bridge(self.config) + if self.config["USE_JOYSTICK"]: + self.unitree_bridge.SetupJoystick( + device_id=self.config["JOYSTICK_DEVICE"], js_type=self.config["JOYSTICK_TYPE"] + ) + + def start(self): + """Main simulation loop""" + import time + sim_cnt = 0 + last_time = time.time() + + print(f"Starting simulation loop. Viewer: {self.sim_env.viewer is not None}") + + try: + while ( + self.sim_env.viewer and self.sim_env.viewer.is_running() + ) or self.sim_env.viewer is None: + # Run simulation step + self.sim_env.sim_step() + + # Update viewer at viewer rate + if sim_cnt % int(self.viewer_dt / self.sim_dt) == 0: + self.sim_env.update_viewer() + + # Calculate reward at reward rate + if sim_cnt % int(self.reward_dt / self.sim_dt) == 0: + self.sim_env.update_reward() + + # Update render caches at image rate + if sim_cnt % int(self.image_dt / self.sim_dt) == 0: + self.sim_env.update_render_caches() + + # Sleep to maintain correct rate (simple timing without ROS) + elapsed = time.time() - last_time + sleep_time = max(0, self.sim_dt - elapsed) + if sleep_time > 0: + time.sleep(sleep_time) + last_time = time.time() + + sim_cnt += 1 + + print(f"Loop exited. Viewer running: {self.sim_env.viewer.is_running() if self.sim_env.viewer else 'No viewer'}") + except KeyboardInterrupt: + # User pressed Ctrl+C - exit cleanly + print("Keyboard interrupt received") + pass + except Exception as e: + print(f"Exception in simulation loop: {e}") + import traceback + traceback.print_exc() + self.close() + + def __del__(self): + """Clean up resources when simulator is deleted""" + self.close() + + def reset(self): + """Reset the simulation. Can be overridden by subclasses.""" + self.sim_env.reset() + + def close(self): + """Close the simulation. Can be overridden by subclasses.""" + try: + # Close viewer + if hasattr(self.sim_env, "viewer") and self.sim_env.viewer is not None: + self.sim_env.viewer.close() + + # Shutdown ROS (if available) + if HAS_RCLPY and rclpy.ok(): + rclpy.shutdown() + except Exception as e: + print(f"Warning during close: {e}") + + def get_privileged_obs(self): + obs = self.sim_env.get_privileged_obs() + # TODO: add ros2 topic to get privileged obs + return obs + + def handle_keyboard_button(self, key): + # Only handles keyboard buttons for default env. + if self.env_name == "default": + self.sim_env.handle_keyboard_button(key) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Robot") + parser.add_argument( + "--config", + type=str, + default="./gr00t_wbc/control/main/teleop/configs/g1_29dof_gear_wbc.yaml", + help="config file", + ) + args = parser.parse_args() + + with open(args.config, "r") as file: + config = yaml.load(file, Loader=yaml.FullLoader) + + if config.get("INTERFACE", None): + ChannelFactoryInitialize(config["DOMAIN_ID"], config["INTERFACE"]) + else: + ChannelFactoryInitialize(config["DOMAIN_ID"]) + + simulation = BaseSimulator(config) + simulation.start_as_thread() diff --git a/mujoco_sim_g1/sim/image_publish_utils.py b/mujoco_sim_g1/sim/image_publish_utils.py new file mode 100644 index 000000000..c9dd78953 --- /dev/null +++ b/mujoco_sim_g1/sim/image_publish_utils.py @@ -0,0 +1,257 @@ +import multiprocessing as mp +from multiprocessing import shared_memory +import time +from typing import Any, Dict + +import numpy as np + +from .sensor_utils import ImageMessageSchema, ImageUtils, SensorServer + + +def get_multiprocessing_info(verbose: bool = True): + """Get information about multiprocessing start methods""" + + if verbose: + print(f"Available start methods: {mp.get_all_start_methods()}") + return mp.get_start_method() + + +class ImagePublishProcess: + """Subprocess for publishing images using shared memory and ZMQ""" + + def __init__( + self, + camera_configs: Dict[str, Any], + image_dt: float, + zmq_port: int = 5555, + start_method: str = "spawn", + verbose: bool = False, + ): + self.camera_configs = camera_configs + self.image_dt = image_dt + self.zmq_port = zmq_port + self.verbose = verbose + self.shared_memory_blocks = {} + self.shared_memory_info = {} + self.process = None + + # Use specific context to avoid global state pollution + self.mp_context = mp.get_context(start_method) + if self.verbose: + print(f"Using multiprocessing context: {start_method}") + + self.stop_event = self.mp_context.Event() + self.data_ready_event = self.mp_context.Event() + + # Ensure events start in correct state + self.stop_event.clear() + self.data_ready_event.clear() + + if self.verbose: + print(f"Initial stop_event state: {self.stop_event.is_set()}") + print(f"Initial data_ready_event state: {self.data_ready_event.is_set()}") + + # Calculate shared memory requirements for each camera + for camera_name, camera_config in camera_configs.items(): + height = camera_config["height"] + width = camera_config["width"] + # RGB image: height * width * 3 (uint8) + size = height * width * 3 + + # Create shared memory block + shm = shared_memory.SharedMemory(create=True, size=size) + self.shared_memory_blocks[camera_name] = shm + self.shared_memory_info[camera_name] = { + "name": shm.name, + "size": size, + "shape": (height, width, 3), + "dtype": np.uint8, + } + + def start_process(self): + """Start the image publishing subprocess""" + if self.verbose: + print(f"Starting subprocess with stop_event state: {self.stop_event.is_set()}") + self.process = self.mp_context.Process( + target=self._image_publish_worker, + args=( + self.shared_memory_info, + self.image_dt, + self.zmq_port, + self.stop_event, + self.data_ready_event, + self.verbose, + ), + ) + self.process.start() + if self.verbose: + print(f"Subprocess started, PID: {self.process.pid}") + + def update_shared_memory(self, render_caches: Dict[str, np.ndarray]): + """Update shared memory with new rendered images""" + images_updated = 0 + for camera_name in self.camera_configs.keys(): + image_key = f"{camera_name}_image" + if image_key in render_caches: + image = render_caches[image_key] + + # Ensure image is uint8 and has correct shape + if image.dtype != np.uint8: + image = (image * 255).astype(np.uint8) + + # Get shared memory array + shm = self.shared_memory_blocks[camera_name] + shared_array = np.ndarray( + self.shared_memory_info[camera_name]["shape"], + dtype=self.shared_memory_info[camera_name]["dtype"], + buffer=shm.buf, + ) + + # Copy image data to shared memory atomically + np.copyto(shared_array, image) + images_updated += 1 + + # Signal that new data is ready only after all images are written + if images_updated > 0: + if self.verbose: + print(f"Main process: Updated {images_updated} images, setting data_ready_event") + self.data_ready_event.set() + elif self.verbose: + print( + "Main process: No images to update. " + "please check if camera configs are provided and the renderer is properly initialized" + ) + + def stop(self): + """Stop the image publishing subprocess""" + if self.verbose: + print("Stopping image publishing subprocess...") + self.stop_event.set() + + if self.process and self.process.is_alive(): + # Give the process time to clean up gracefully + self.process.join(timeout=5) + if self.process.is_alive(): + if self.verbose: + print("Subprocess didn't stop gracefully, terminating...") + self.process.terminate() + self.process.join(timeout=2) + if self.process.is_alive(): + if self.verbose: + print("Force killing subprocess...") + self.process.kill() + self.process.join() + + # Clean up shared memory + for camera_name, shm in self.shared_memory_blocks.items(): + try: + shm.close() + shm.unlink() + if self.verbose: + print(f"Cleaned up shared memory for {camera_name}") + except Exception as e: + if self.verbose: + print(f"Warning: Failed to cleanup shared memory for {camera_name}: {e}") + + self.shared_memory_blocks.clear() + if self.verbose: + print("Image publishing subprocess stopped and cleaned up") + + @staticmethod + def _image_publish_worker( + shared_memory_info, image_dt, zmq_port, stop_event, data_ready_event, verbose + ): + """Worker function that runs in the subprocess""" + # Import dependencies within worker (needed for multiprocessing spawn mode) + from .sensor_utils import ImageMessageSchema, ImageUtils, SensorServer + + if verbose: + print(f"Worker started! PID: {__import__('os').getpid()}") + print(f"Worker stop_event state at start: {stop_event.is_set()}") + print(f"Worker data_ready_event state at start: {data_ready_event.is_set()}") + + try: + # Initialize ZMQ sensor server + sensor_server = SensorServer() + sensor_server.start_server(port=zmq_port) + + # Connect to shared memory blocks + shared_arrays = {} + shm_blocks = {} + for camera_name, info in shared_memory_info.items(): + shm = shared_memory.SharedMemory(name=info["name"]) + shm_blocks[camera_name] = shm + shared_arrays[camera_name] = np.ndarray( + info["shape"], dtype=info["dtype"], buffer=shm.buf + ) + + print( + f"Image publishing subprocess started with {len(shared_arrays)} cameras on ZMQ port {zmq_port}" + ) + + loop_count = 0 + last_data_time = time.time() + + while not stop_event.is_set(): + loop_count += 1 + + # Wait for new data with shorter timeout for better responsiveness + timeout = min(image_dt, 0.1) # Max 100ms timeout + data_available = data_ready_event.wait(timeout=timeout) + + current_time = time.time() + + if data_available: + data_ready_event.clear() + if loop_count % 50 == 0: + print("Image publish frequency: ", 1 / (current_time - last_data_time)) + last_data_time = current_time + + # Collect all camera images and serialize them + try: + # Copy all images atomically at once + image_copies = {name: arr.copy() for name, arr in shared_arrays.items()} + + # Create message with all camera images + message_dict = { + "images": image_copies, + "timestamps": {name: current_time for name in image_copies.keys()}, + } + + # Create ImageMessageSchema and serialize + image_msg = ImageMessageSchema( + timestamps=message_dict.get("timestamps"), + images=message_dict.get("images", None), + ) + + # Serialize and send via ZMQ + serialized_data = image_msg.serialize() + + # Add individual camera images to the message + for camera_name, image_copy in image_copies.items(): + serialized_data[f"{camera_name}"] = ImageUtils.encode_image(image_copy) + + sensor_server.send_message(serialized_data) + + except Exception as e: + print(f"Error publishing images: {e}") + + elif verbose and loop_count % 10 == 0: + print(f"Subprocess: Still waiting for data... (iteration {loop_count})") + + # Small sleep to prevent busy waiting when no data + if not data_available: + time.sleep(0.001) + + except KeyboardInterrupt: + print("Image publisher interrupted by user") + finally: + # Clean up + try: + for shm in shm_blocks.values(): + shm.close() + sensor_server.stop_server() + except Exception as e: + print(f"Error during subprocess cleanup: {e}") + if verbose: + print("Image publish subprocess stopped") diff --git a/mujoco_sim_g1/sim/metric_utils.py b/mujoco_sim_g1/sim/metric_utils.py new file mode 100644 index 000000000..369b8797c --- /dev/null +++ b/mujoco_sim_g1/sim/metric_utils.py @@ -0,0 +1,71 @@ +from typing import List, Tuple + +import mujoco + +from .sim_utilts import get_body_geom_ids + + +def check_contact( + mj_model: mujoco.MjModel, + mj_data: mujoco.MjData, + bodies_1: List[str] | str, + bodies_2: List[str] | str, + return_all_contact_bodies: bool = False, +) -> Tuple[bool, List[Tuple[str, str]]] | bool: + """ + Finds contact between two body groups. Any geom in the body is considered to be in contact. + Args: + mj_model (MujocoModel): Current simulation object + mj_data (MjData): Current simulation data + bodies_1 (str or list of int): an individual body name or list of body names. + bodies_2 (str or list of int): another individual body name or list of body names. + Returns: + bool: True if any body in @bodies_1 is in contact with any body in @bodies_2. + """ + if isinstance(bodies_1, str): + bodies_1 = [bodies_1] + if isinstance(bodies_2, str): + bodies_2 = [bodies_2] + + geoms_1 = [get_body_geom_ids(mj_model, mj_model.body(g).id) for g in bodies_1] + geoms_1 = [g for geom_list in geoms_1 for g in geom_list] + geoms_2 = [get_body_geom_ids(mj_model, mj_model.body(g).id) for g in bodies_2] + geoms_2 = [g for geom_list in geoms_2 for g in geom_list] + contact_bodies = [] + for i in range(mj_data.ncon): + contact = mj_data.contact[i] + # check contact geom in geoms + c1_in_g1 = contact.geom1 in geoms_1 + c2_in_g2 = contact.geom2 in geoms_2 if geoms_2 is not None else True + # check contact geom in geoms (flipped) + c2_in_g1 = contact.geom2 in geoms_1 + c1_in_g2 = contact.geom1 in geoms_2 if geoms_2 is not None else True + if (c1_in_g1 and c2_in_g2) or (c1_in_g2 and c2_in_g1): + contact_bodies.append( + ( + mj_model.body(mj_model.geom(contact.geom1).bodyid).name, + mj_model.body(mj_model.geom(contact.geom2).bodyid).name, + ) + ) + if not return_all_contact_bodies: + break + if return_all_contact_bodies: + return len(contact_bodies) > 0, set(contact_bodies) + else: + return len(contact_bodies) > 0 + + +def check_height( + mj_model: mujoco.MjModel, + mj_data: mujoco.MjData, + geom_name: str, + lower_bound: float = -float("inf"), + upper_bound: float = float("inf"), +): + """ + Checks if the height of a geom is greater than a given height. + """ + geom_id = mj_model.geom(geom_name).id + return ( + mj_data.geom_xpos[geom_id][2] < upper_bound and mj_data.geom_xpos[geom_id][2] > lower_bound + ) diff --git a/mujoco_sim_g1/sim/sensor_utils.py b/mujoco_sim_g1/sim/sensor_utils.py new file mode 100644 index 000000000..7b205c2e6 --- /dev/null +++ b/mujoco_sim_g1/sim/sensor_utils.py @@ -0,0 +1,130 @@ +"""Standalone sensor utilities for camera image publishing via ZMQ""" +import base64 +from dataclasses import dataclass +from typing import Any, Dict + +import cv2 +import msgpack +import msgpack_numpy as m +import numpy as np +import zmq + + +@dataclass +class ImageMessageSchema: + """ + Standardized message schema for image data. + Used to serialize/deserialize image data for network transmission. + """ + + timestamps: Dict[str, float] + """Dictionary of timestamps, keyed by image identifier (e.g., {"ego_view": 123.45})""" + images: Dict[str, np.ndarray] + """Dictionary of images, keyed by image identifier (e.g., {"ego_view": array})""" + + def serialize(self) -> Dict[str, Any]: + """Serialize the message for transmission.""" + serialized_msg = {"timestamps": self.timestamps, "images": {}} + for key, image in self.images.items(): + serialized_msg["images"][key] = ImageUtils.encode_image(image) + return serialized_msg + + @staticmethod + def deserialize(data: Dict[str, Any]) -> "ImageMessageSchema": + """Deserialize received message data.""" + timestamps = data.get("timestamps", {}) + images = {} + for key, value in data.get("images", {}).items(): + if isinstance(value, str): + images[key] = ImageUtils.decode_image(value) + else: + images[key] = value + return ImageMessageSchema(timestamps=timestamps, images=images) + + def asdict(self) -> Dict[str, Any]: + """Convert to dictionary format.""" + return {"timestamps": self.timestamps, "images": self.images} + + +class SensorServer: + """ZMQ-based sensor server for publishing camera images""" + + def start_server(self, port: int): + self.context = zmq.Context() + self.socket = self.context.socket(zmq.PUB) + self.socket.setsockopt(zmq.SNDHWM, 20) # high water mark + self.socket.setsockopt(zmq.LINGER, 0) + self.socket.bind(f"tcp://*:{port}") + print(f"Sensor server running at tcp://*:{port}") + + self.message_sent = 0 + self.message_dropped = 0 + + def stop_server(self): + self.socket.close() + self.context.term() + + def send_message(self, data: Dict[str, Any]): + try: + packed = msgpack.packb(data, use_bin_type=True) + self.socket.send(packed, flags=zmq.NOBLOCK) + except zmq.Again: + self.message_dropped += 1 + print(f"[Warning] message dropped: {self.message_dropped}") + self.message_sent += 1 + + if self.message_sent % 100 == 0: + print( + f"[Sensor server] Message sent: {self.message_sent}, message dropped: {self.message_dropped}" + ) + + +class SensorClient: + """ZMQ-based sensor client for subscribing to camera images""" + + def start_client(self, server_ip: str, port: int): + self.context = zmq.Context() + self.socket = self.context.socket(zmq.SUB) + self.socket.setsockopt_string(zmq.SUBSCRIBE, "") + self.socket.setsockopt(zmq.CONFLATE, True) # last msg only. + self.socket.setsockopt(zmq.RCVHWM, 3) # queue size 3 for receive buffer + self.socket.connect(f"tcp://{server_ip}:{port}") + + def stop_client(self): + self.socket.close() + self.context.term() + + def receive_message(self): + packed = self.socket.recv() + return msgpack.unpackb(packed, object_hook=m.decode) + + +class ImageUtils: + """Utilities for encoding/decoding images for network transmission""" + + @staticmethod + def encode_image(image: np.ndarray) -> str: + """Encode numpy image to base64-encoded JPEG string""" + _, color_buffer = cv2.imencode(".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), 80]) + return base64.b64encode(color_buffer).decode("utf-8") + + @staticmethod + def encode_depth_image(image: np.ndarray) -> str: + """Encode depth image to base64-encoded PNG string""" + depth_compressed = cv2.imencode(".png", image)[1].tobytes() + return base64.b64encode(depth_compressed).decode("utf-8") + + @staticmethod + def decode_image(image: str) -> np.ndarray: + """Decode base64-encoded JPEG string to numpy image""" + color_data = base64.b64decode(image) + color_array = np.frombuffer(color_data, dtype=np.uint8) + return cv2.imdecode(color_array, cv2.IMREAD_COLOR) + + @staticmethod + def decode_depth_image(image: str) -> np.ndarray: + """Decode base64-encoded PNG string to depth image""" + depth_data = base64.b64decode(image) + depth_array = np.frombuffer(depth_data, dtype=np.uint8) + return cv2.imdecode(depth_array, cv2.IMREAD_UNCHANGED) + diff --git a/mujoco_sim_g1/sim/sim_utilts.py b/mujoco_sim_g1/sim/sim_utilts.py new file mode 100644 index 000000000..e032eb6d8 --- /dev/null +++ b/mujoco_sim_g1/sim/sim_utilts.py @@ -0,0 +1,96 @@ +""" +Utility functions for working with Mujoco models. +copied from https://github.com/kevinzakka/mink/blob/main/mink/utils.py +""" + +from typing import List + +import mujoco + + +def get_body_body_ids(model: mujoco.MjModel, body_id: int) -> List[int]: + """Get immediate children bodies belonging to a given body. + + Args: + model: Mujoco model. + body_id: ID of body. + + Returns: + A List containing all child body ids. + """ + return [ + i + for i in range(model.nbody) + if model.body_parentid[i] == body_id and body_id != i # Exclude the body itself. + ] + + +def get_subtree_body_ids(model: mujoco.MjModel, body_id: int) -> List[int]: + """Get all bodies belonging to subtree starting at a given body. + + Args: + model: Mujoco model. + body_id: ID of body where subtree starts. + + Returns: + A List containing all subtree body ids. + """ + body_ids: List[int] = [] + stack = [body_id] + while stack: + body_id = stack.pop() + body_ids.append(body_id) + stack += get_body_body_ids(model, body_id) + return body_ids + + +def get_subtree_body_names(model: mujoco.MjModel, body_id: int) -> List[str]: + """Get all bodies belonging to subtree starting at a given body. + Args: + model: Mujoco model. + body_id: ID of body where subtree starts. + + Returns: + A List containing all subtree body names. + """ + return [model.body(i).name for i in get_subtree_body_ids(model, body_id)] + + +def get_body_geom_ids(model: mujoco.MjModel, body_id: int) -> List[int]: + """Get immediate geoms belonging to a given body. + + Here, immediate geoms are those directly attached to the body and not its + descendants. + + Args: + model: Mujoco model. + body_id: ID of body. + + Returns: + A list containing all body geom ids. + """ + geom_start = model.body_geomadr[body_id] + geom_end = geom_start + model.body_geomnum[body_id] + return list(range(geom_start, geom_end)) + + +def get_subtree_geom_ids(model: mujoco.MjModel, body_id: int) -> List[int]: + """Get all geoms belonging to subtree starting at a given body. + + Here, a subtree is defined as the kinematic tree starting at the body and including + all its descendants. + + Args: + model: Mujoco model. + body_id: ID of body where subtree starts. + + Returns: + A list containing all subtree geom ids. + """ + geom_ids: List[int] = [] + stack = [body_id] + while stack: + body_id = stack.pop() + geom_ids.extend(get_body_geom_ids(model, body_id)) + stack += get_body_body_ids(model, body_id) + return geom_ids diff --git a/mujoco_sim_g1/sim/simulator_factory.py b/mujoco_sim_g1/sim/simulator_factory.py new file mode 100644 index 000000000..3dca58dc2 --- /dev/null +++ b/mujoco_sim_g1/sim/simulator_factory.py @@ -0,0 +1,148 @@ +import time +from typing import Any, Dict + +from unitree_sdk2py.core.channel import ChannelFactoryInitialize + +from .base_sim import BaseSimulator + + +def init_channel(config: Dict[str, Any]) -> None: + """ + Initialize the communication channel for simulator/robot communication. + + Args: + config: Configuration dictionary containing DOMAIN_ID and optionally INTERFACE + """ + if config.get("INTERFACE", None): + ChannelFactoryInitialize(config["DOMAIN_ID"], config["INTERFACE"]) + else: + ChannelFactoryInitialize(config["DOMAIN_ID"]) + + +class SimulatorFactory: + """Factory class for creating different types of simulators.""" + + @staticmethod + def create_simulator(config: Dict[str, Any], env_name: str = "default", **kwargs): + """ + Create a simulator based on the configuration. + + Args: + config: Configuration dictionary containing SIMULATOR type + env_name: Environment name + **kwargs: Additional keyword arguments for specific simulators + """ + simulator_type = config.get("SIMULATOR", "mujoco") + if simulator_type == "mujoco": + return SimulatorFactory._create_mujoco_simulator(config, env_name, **kwargs) + elif simulator_type == "robocasa": + return SimulatorFactory._create_robocasa_simulator(config, env_name, **kwargs) + else: + print( + f"Warning: Invalid simulator type: {simulator_type}. " + "If you are using run_sim_loop, please ignore this warning." + ) + return None + + @staticmethod + def _create_mujoco_simulator(config: Dict[str, Any], env_name: str = "default", **kwargs): + """Create a MuJoCo simulator instance.""" + camera_configs = kwargs.pop("camera_configs", {}) + if len(camera_configs) > 0: + print(f"Debug: SimulatorFactory received {len(camera_configs)} camera config(s)") + + env_kwargs = dict( + onscreen=kwargs.pop("onscreen", True), + offscreen=kwargs.pop("offscreen", False), + camera_configs=camera_configs, + ) + return BaseSimulator(config=config, env_name=env_name, **env_kwargs) + + @staticmethod + def _create_robocasa_simulator(config: Dict[str, Any], env_name: str = "default", **kwargs): + """Create a RoboCasa simulator instance.""" + from gr00t_wbc.control.envs.g1.sim.robocasa_sim import RoboCasaG1EnvServer + from gr00t_wbc.control.envs.robocasa.utils.controller_utils import ( + update_robosuite_controller_configs, + ) + from gr00t_wbc.control.envs.robocasa.utils.sim_utils import change_simulation_timestep + + change_simulation_timestep(config["SIMULATE_DT"]) + + # Use default environment if not specified + if env_name == "default": + env_name = "GroundOnly" + + # Get or create controller configurations + controller_configs = kwargs.get("controller_configs") + if controller_configs is None: + wbc_version = kwargs.get("wbc_version", "gear_wbc") + controller_configs = update_robosuite_controller_configs("G1", wbc_version) + + # Build environment kwargs + env_kwargs = dict( + onscreen=kwargs.pop("onscreen", True), + offscreen=kwargs.pop("offscreen", False), + camera_names=kwargs.pop("camera_names", None), + camera_heights=kwargs.pop("camera_heights", None), + camera_widths=kwargs.pop("camera_widths", None), + control_freq=kwargs.pop("control_freq", 50), + controller_configs=controller_configs, + ik_indicator=kwargs.pop("ik_indicator", False), + randomize_cameras=kwargs.pop("randomize_cameras", True), + ) + + kwargs.update( + { + "verbose": config.pop("verbose", False), + "sim_freq": 1 / config.pop("SIMULATE_DT"), + } + ) + + return RoboCasaG1EnvServer( + env_name=env_name, + wbc_config=config, + env_kwargs=env_kwargs, + **kwargs, + ) + + @staticmethod + def start_simulator( + simulator, + as_thread: bool = True, + enable_image_publish: bool = False, + mp_start_method: str = "spawn", + camera_port: int = 5555, + ): + """ + Start the simulator either as a thread or as a separate process. + + Args: + simulator: The simulator instance to start + config: Configuration dictionary + as_thread: If True, start as thread; if False, start as subprocess + enable_offscreen: If True and not as_thread, start image publishing + """ + + if as_thread: + simulator.start_as_thread() + else: + # Wrap in try-except to make sure simulator is properly closed upon exit. + try: + if enable_image_publish: + simulator.start_image_publish_subprocess( + start_method=mp_start_method, + camera_port=camera_port, + ) + time.sleep(1) + simulator.start() + except KeyboardInterrupt: + print("+++++Simulator interrupted by user.") + except Exception as e: + print(f"++++error in simulator: {e} ++++") + finally: + print("++++closing simulator ++++") + simulator.close() + + # Allow simulator to initialize + time.sleep(1) diff --git a/mujoco_sim_g1/sim/unitree_sdk2py_bridge.py b/mujoco_sim_g1/sim/unitree_sdk2py_bridge.py new file mode 100644 index 000000000..6cc596611 --- /dev/null +++ b/mujoco_sim_g1/sim/unitree_sdk2py_bridge.py @@ -0,0 +1,497 @@ +import sys +import threading +from typing import Dict, Tuple + +import glfw +from loguru import logger +import mujoco +import numpy as np +import pygame +import scipy.spatial.transform +from termcolor import colored +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber +from unitree_sdk2py.idl.default import ( + unitree_go_msg_dds__WirelessController_, + unitree_hg_msg_dds__HandCmd_ as HandCmd_default, + unitree_hg_msg_dds__HandState_ as HandState_default, +) +from unitree_sdk2py.idl.unitree_go.msg.dds_ import WirelessController_ +try: + from unitree_sdk2py.idl.unitree_hg.msg.dds_ import HandCmd_, HandState_, OdoState_ + HAS_ODOSTATE = True +except ImportError: + from unitree_sdk2py.idl.unitree_hg.msg.dds_ import HandCmd_, HandState_ + OdoState_ = None + HAS_ODOSTATE = False + print("Warning: OdoState_ not available in unitree_sdk2py") + + +class UnitreeSdk2Bridge: + """ + This class is responsible for bridging the Unitree SDK2 with the Gr00t environment. + It is responsible for sending and receiving messages to and from the Unitree SDK2. + Both the body and hand are supported. + """ + + def __init__(self, config): + # Note that we do not give the mjdata and mjmodel to the UnitreeSdk2Bridge. + # It is unsafe and would be unflexible if we use a hand-plugged robot model + + robot_type = config["ROBOT_TYPE"] + if "g1" in robot_type or "h1-2" in robot_type: + from unitree_sdk2py.idl.default import ( + unitree_hg_msg_dds__IMUState_ as IMUState_default, + unitree_hg_msg_dds__LowCmd_, + unitree_hg_msg_dds__LowState_ as LowState_default, + ) + try: + from unitree_sdk2py.idl.default import unitree_hg_msg_dds__OdoState_ as OdoState_default + except ImportError: + OdoState_default = None + from unitree_sdk2py.idl.unitree_hg.msg.dds_ import IMUState_, LowCmd_, LowState_ + + self.low_cmd = unitree_hg_msg_dds__LowCmd_() + elif "h1" == robot_type or "go2" == robot_type: + from unitree_sdk2py.idl.default import ( + unitree_go_msg_dds__LowCmd_, + unitree_go_msg_dds__LowState_ as LowState_default, + unitree_hg_msg_dds__IMUState_ as IMUState_default, + ) + from unitree_sdk2py.idl.unitree_go.msg.dds_ import IMUState_, LowCmd_, LowState_ + + self.low_cmd = unitree_go_msg_dds__LowCmd_() + else: + raise ValueError(f"Invalid robot type '{robot_type}'. Expected 'g1', 'h1', or 'go2'.") + + self.num_body_motor = config["NUM_MOTORS"] + self.num_hand_motor = config.get("NUM_HAND_MOTORS", 0) + self.use_sensor = config["USE_SENSOR"] + + self.have_imu_ = False + self.have_frame_sensor_ = False + # if self.use_sensor: + # MOTOR_SENSOR_NUM = 3 + # self.dim_motor_sensor = MOTOR_SENSOR_NUM * self.num_motor + # # Check sensor + # for i in range(self.dim_motor_sensor, self.mj_model.nsensor): + # name = mujoco.mj_id2name(self.mj_model, mujoco._enums.mjtObj.mjOBJ_SENSOR, i) + # if name == "imu_quat": + # self.have_imu_ = True + # if name == "frame_pos": + # self.have_frame_sensor_ = True + + # Unitree sdk2 message + self.low_state = LowState_default() + self.low_state_puber = ChannelPublisher("rt/lowstate", LowState_) + self.low_state_puber.Init() + + # Only create odo_state for supported robot types (if available) + if ("g1" in robot_type or "h1-2" in robot_type) and HAS_ODOSTATE and OdoState_default: + self.odo_state = OdoState_default() + self.odo_state_puber = ChannelPublisher("rt/odostate", OdoState_) + self.odo_state_puber.Init() + else: + self.odo_state = None + self.odo_state_puber = None + self.torso_imu_state = IMUState_default() + self.torso_imu_puber = ChannelPublisher("rt/secondary_imu", IMUState_) + self.torso_imu_puber.Init() + + self.left_hand_state = HandState_default() + self.left_hand_state_puber = ChannelPublisher("rt/dex3/left/state", HandState_) + self.left_hand_state_puber.Init() + self.right_hand_state = HandState_default() + self.right_hand_state_puber = ChannelPublisher("rt/dex3/right/state", HandState_) + self.right_hand_state_puber.Init() + + self.low_cmd_suber = ChannelSubscriber("rt/lowcmd", LowCmd_) + self.low_cmd_suber.Init(self.LowCmdHandler, 1) + + self.left_hand_cmd = HandCmd_default() + self.left_hand_cmd_suber = ChannelSubscriber("rt/dex3/left/cmd", HandCmd_) + self.left_hand_cmd_suber.Init(self.LeftHandCmdHandler, 1) + self.right_hand_cmd = HandCmd_default() + self.right_hand_cmd_suber = ChannelSubscriber("rt/dex3/right/cmd", HandCmd_) + self.right_hand_cmd_suber.Init(self.RightHandCmdHandler, 1) + + self.low_cmd_lock = threading.Lock() + self.left_hand_cmd_lock = threading.Lock() + self.right_hand_cmd_lock = threading.Lock() + + self.wireless_controller = unitree_go_msg_dds__WirelessController_() + self.wireless_controller_puber = ChannelPublisher( + "rt/wirelesscontroller", WirelessController_ + ) + self.wireless_controller_puber.Init() + + # joystick + self.key_map = { + "R1": 0, + "L1": 1, + "start": 2, + "select": 3, + "R2": 4, + "L2": 5, + "F1": 6, + "F2": 7, + "A": 8, + "B": 9, + "X": 10, + "Y": 11, + "up": 12, + "right": 13, + "down": 14, + "left": 15, + } + self.joystick = None + + # Store config for initialization + self.config = config + + self.reset() + + # Initialize motors with default KP/KD from config to make robot stiff at startup + self._initialize_motor_defaults() + + def _initialize_motor_defaults(self): + """Initialize motor commands with default KP/KD and joint positions""" + if "MOTOR_KP" in self.config and "MOTOR_KD" in self.config: + motor_kp = self.config["MOTOR_KP"] + motor_kd = self.config["MOTOR_KD"] + default_dof_angles = self.config.get("DEFAULT_DOF_ANGLES", [0.0] * self.num_body_motor) + + for i in range(min(self.num_body_motor, len(motor_kp))): + self.low_cmd.motor_cmd[i].kp = motor_kp[i] + self.low_cmd.motor_cmd[i].kd = motor_kd[i] + self.low_cmd.motor_cmd[i].q = default_dof_angles[i] if i < len(default_dof_angles) else 0.0 + self.low_cmd.motor_cmd[i].dq = 0.0 + self.low_cmd.motor_cmd[i].tau = 0.0 + + print(f"✓ Initialized {self.num_body_motor} motors with default KP/KD gains") + else: + print("⚠ Warning: MOTOR_KP/MOTOR_KD not found in config, robot will be limp at startup") + + def reset(self): + with self.low_cmd_lock: + self.low_cmd_received = False + self.new_low_cmd = False + with self.left_hand_cmd_lock: + self.left_hand_cmd_received = False + self.new_left_hand_cmd = False + with self.right_hand_cmd_lock: + self.right_hand_cmd_received = False + self.new_right_hand_cmd = False + + def LowCmdHandler(self, msg): + with self.low_cmd_lock: + self.low_cmd = msg + self.low_cmd_received = True + self.new_low_cmd = True + + def LeftHandCmdHandler(self, msg): + with self.left_hand_cmd_lock: + self.left_hand_cmd = msg + self.left_hand_cmd_received = True + self.new_left_hand_cmd = True + + def RightHandCmdHandler(self, msg): + with self.right_hand_cmd_lock: + self.right_hand_cmd = msg + self.right_hand_cmd_received = True + self.new_right_hand_cmd = True + + def cmd_received(self): + with self.low_cmd_lock: + low_cmd_received = self.low_cmd_received + with self.left_hand_cmd_lock: + left_hand_cmd_received = self.left_hand_cmd_received + with self.right_hand_cmd_lock: + right_hand_cmd_received = self.right_hand_cmd_received + return low_cmd_received or left_hand_cmd_received or right_hand_cmd_received + + def PublishLowState(self, obs: Dict[str, any]): + # publish body state + if self.use_sensor: + raise NotImplementedError("Sensor data is not implemented yet.") + else: + for i in range(self.num_body_motor): + self.low_state.motor_state[i].q = obs["body_q"][i] + self.low_state.motor_state[i].dq = obs["body_dq"][i] + self.low_state.motor_state[i].ddq = obs["body_ddq"][i] + self.low_state.motor_state[i].tau_est = obs["body_tau_est"][i] + + if self.use_sensor and self.have_frame_sensor_: + raise NotImplementedError("Frame sensor data is not implemented yet.") + else: + # Get data from ground truth + # Publish odo state only if available + if self.odo_state is not None: + self.odo_state.position[:] = obs["floating_base_pose"][:3] + self.odo_state.linear_velocity[:] = obs["floating_base_vel"][:3] + self.odo_state.orientation[:] = obs["floating_base_pose"][3:7] + self.odo_state.angular_velocity[:] = obs["floating_base_vel"][3:6] + # quaternion: w, x, y, z + self.low_state.imu_state.quaternion[:] = obs["floating_base_pose"][3:7] + # angular velocity + self.low_state.imu_state.gyroscope[:] = obs["floating_base_vel"][3:6] + # linear acceleration + self.low_state.imu_state.accelerometer[:] = obs["floating_base_acc"][:3] + + self.torso_imu_state.quaternion[:] = obs["secondary_imu_quat"] + self.torso_imu_state.gyroscope[:] = obs["secondary_imu_vel"][3:6] + + # acceleration: x, y, z (only available when frame sensor is enabled) + if self.have_frame_sensor_: + raise NotImplementedError("Frame sensor data is not implemented yet.") + self.low_state.tick = int(obs["time"] * 1e3) + self.low_state_puber.Write(self.low_state) + + # Publish odo state only if available + if self.odo_state is not None and self.odo_state_puber is not None: + self.odo_state.tick = int(obs["time"] * 1e3) + self.odo_state_puber.Write(self.odo_state) + + self.torso_imu_puber.Write(self.torso_imu_state) + + # publish hand state + for i in range(self.num_hand_motor): + self.left_hand_state.motor_state[i].q = obs["left_hand_q"][i] + self.left_hand_state.motor_state[i].dq = obs["left_hand_dq"][i] + self.left_hand_state_puber.Write(self.left_hand_state) + + for i in range(self.num_hand_motor): + self.right_hand_state.motor_state[i].q = obs["right_hand_q"][i] + self.right_hand_state.motor_state[i].dq = obs["right_hand_dq"][i] + self.right_hand_state_puber.Write(self.right_hand_state) + + def GetAction(self) -> Tuple[np.ndarray, bool, bool]: + with self.low_cmd_lock: + body_q = [self.low_cmd.motor_cmd[i].q for i in range(self.num_body_motor)] + with self.left_hand_cmd_lock: + left_hand_q = [self.left_hand_cmd.motor_cmd[i].q for i in range(self.num_hand_motor)] + with self.right_hand_cmd_lock: + right_hand_q = [self.right_hand_cmd.motor_cmd[i].q for i in range(self.num_hand_motor)] + with self.low_cmd_lock and self.left_hand_cmd_lock and self.right_hand_cmd_lock: + is_new_action = self.new_low_cmd and self.new_left_hand_cmd and self.new_right_hand_cmd + if is_new_action: + self.new_low_cmd = False + self.new_left_hand_cmd = False + self.new_right_hand_cmd = False + + return ( + np.concatenate([body_q[:-7], left_hand_q, body_q[-7:], right_hand_q]), + self.cmd_received(), + is_new_action, + ) + + def PublishWirelessController(self): + if self.joystick is not None: + pygame.event.get() + key_state = [0] * 16 + key_state[self.key_map["R1"]] = self.joystick.get_button(self.button_id["RB"]) + key_state[self.key_map["L1"]] = self.joystick.get_button(self.button_id["LB"]) + key_state[self.key_map["start"]] = self.joystick.get_button(self.button_id["START"]) + key_state[self.key_map["select"]] = self.joystick.get_button(self.button_id["SELECT"]) + key_state[self.key_map["R2"]] = self.joystick.get_axis(self.axis_id["RT"]) > 0 + key_state[self.key_map["L2"]] = self.joystick.get_axis(self.axis_id["LT"]) > 0 + key_state[self.key_map["F1"]] = 0 + key_state[self.key_map["F2"]] = 0 + key_state[self.key_map["A"]] = self.joystick.get_button(self.button_id["A"]) + key_state[self.key_map["B"]] = self.joystick.get_button(self.button_id["B"]) + key_state[self.key_map["X"]] = self.joystick.get_button(self.button_id["X"]) + key_state[self.key_map["Y"]] = self.joystick.get_button(self.button_id["Y"]) + key_state[self.key_map["up"]] = self.joystick.get_hat(0)[1] > 0 + key_state[self.key_map["right"]] = self.joystick.get_hat(0)[0] > 0 + key_state[self.key_map["down"]] = self.joystick.get_hat(0)[1] < 0 + key_state[self.key_map["left"]] = self.joystick.get_hat(0)[0] < 0 + + key_value = 0 + for i in range(16): + key_value += key_state[i] << i + + self.wireless_controller.keys = key_value + self.wireless_controller.lx = self.joystick.get_axis(self.axis_id["LX"]) + self.wireless_controller.ly = -self.joystick.get_axis(self.axis_id["LY"]) + self.wireless_controller.rx = self.joystick.get_axis(self.axis_id["RX"]) + self.wireless_controller.ry = -self.joystick.get_axis(self.axis_id["RY"]) + + self.wireless_controller_puber.Write(self.wireless_controller) + + def SetupJoystick(self, device_id=0, js_type="xbox"): + pygame.init() + pygame.joystick.init() + joystick_count = pygame.joystick.get_count() + if joystick_count > 0: + self.joystick = pygame.joystick.Joystick(device_id) + self.joystick.init() + else: + print("No gamepad detected.") + sys.exit() + + if js_type == "xbox": + if sys.platform.startswith("linux"): + self.axis_id = { + "LX": 0, # Left stick axis x + "LY": 1, # Left stick axis y + "RX": 3, # Right stick axis x + "RY": 4, # Right stick axis y + "LT": 2, # Left trigger + "RT": 5, # Right trigger + "DX": 6, # Directional pad x + "DY": 7, # Directional pad y + } + self.button_id = { + "X": 2, + "Y": 3, + "B": 1, + "A": 0, + "LB": 4, + "RB": 5, + "SELECT": 6, + "START": 7, + "XBOX": 8, + "LSB": 9, + "RSB": 10, + } + elif sys.platform == "darwin": + self.axis_id = { + "LX": 0, # Left stick axis x + "LY": 1, # Left stick axis y + "RX": 2, # Right stick axis x + "RY": 3, # Right stick axis y + "LT": 4, # Left trigger + "RT": 5, # Right trigger + } + self.button_id = { + "X": 2, + "Y": 3, + "B": 1, + "A": 0, + "LB": 9, + "RB": 10, + "SELECT": 4, + "START": 6, + "XBOX": 5, + "LSB": 7, + "RSB": 8, + "DYU": 11, + "DYD": 12, + "DXL": 13, + "DXR": 14, + } + else: + print("Unsupported OS. ") + + elif js_type == "switch": + # Yuanhang: may differ for different OS, need to be checked + self.axis_id = { + "LX": 0, # Left stick axis x + "LY": 1, # Left stick axis y + "RX": 2, # Right stick axis x + "RY": 3, # Right stick axis y + "LT": 5, # Left trigger + "RT": 4, # Right trigger + "DX": 6, # Directional pad x + "DY": 7, # Directional pad y + } + + self.button_id = { + "X": 3, + "Y": 4, + "B": 1, + "A": 0, + "LB": 6, + "RB": 7, + "SELECT": 10, + "START": 11, + } + else: + print("Unsupported gamepad. ") + + def PrintSceneInformation(self): + print(" ") + logger.info(colored("<<------------- Link ------------->>", "green")) + for i in range(self.mj_model.nbody): + name = mujoco.mj_id2name(self.mj_model, mujoco._enums.mjtObj.mjOBJ_BODY, i) + if name: + logger.info(f"link_index: {i}, name: {name}") + print(" ") + + logger.info(colored("<<------------- Joint ------------->>", "green")) + for i in range(self.mj_model.njnt): + name = mujoco.mj_id2name(self.mj_model, mujoco._enums.mjtObj.mjOBJ_JOINT, i) + if name: + logger.info(f"joint_index: {i}, name: {name}") + print(" ") + + logger.info(colored("<<------------- Actuator ------------->>", "green")) + for i in range(self.mj_model.nu): + name = mujoco.mj_id2name(self.mj_model, mujoco._enums.mjtObj.mjOBJ_ACTUATOR, i) + if name: + logger.info(f"actuator_index: {i}, name: {name}") + print(" ") + + logger.info(colored("<<------------- Sensor ------------->>", "green")) + index = 0 + for i in range(self.mj_model.nsensor): + name = mujoco.mj_id2name(self.mj_model, mujoco._enums.mjtObj.mjOBJ_SENSOR, i) + if name: + logger.info( + f"sensor_index: {index}, name: {name}, dim: {self.mj_model.sensor_dim[i]}" + ) + index = index + self.mj_model.sensor_dim[i] + print(" ") + + +class ElasticBand: + """ + ref: https://github.com/unitreerobotics/unitree_mujoco + """ + + def __init__(self): + self.kp_pos = 10000 + self.kd_pos = 1000 + self.kp_ang = 1000 + self.kd_ang = 10 + self.point = np.array([0, 0, 1]) + self.length = 0 + self.enable = True + + def Advance(self, pose): + """ + Args: + pose: 13D array containing: + - pose[0:3]: position in world frame + - pose[3:7]: quaternion [w,x,y,z] in world frame + - pose[7:10]: linear velocity in world frame + - pose[10:13]: angular velocity in world frame + Returns: + np.ndarray: 6D vector [fx, fy, fz, tx, ty, tz] + """ + pos = pose[0:3] + quat = pose[3:7] + lin_vel = pose[7:10] + ang_vel = pose[10:13] + + δx = self.point - pos + f = self.kp_pos * (δx + np.array([0, 0, self.length])) + self.kd_pos * (0 - lin_vel) + + # --- Orientation PD control for torque --- + quat = np.array([quat[1], quat[2], quat[3], quat[0]]) # reorder to [x,y,z,w] for scipy + rot = scipy.spatial.transform.Rotation.from_quat(quat) + rotvec = rot.as_rotvec() # axis-angle error + torque = -self.kp_ang * rotvec - self.kd_ang * ang_vel + + return np.concatenate([f, torque]) + + def MujuocoKeyCallback(self, key): + if key == glfw.KEY_7: + self.length -= 0.1 + if key == glfw.KEY_8: + self.length += 0.1 + if key == glfw.KEY_9: + self.enable = not self.enable + + def handle_keyboard_button(self, key): + if key == "9": + self.enable = not self.enable diff --git a/mujoco_sim_g1/view_cameras.py b/mujoco_sim_g1/view_cameras.py new file mode 100644 index 000000000..6467c0aeb --- /dev/null +++ b/mujoco_sim_g1/view_cameras.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +""" +Camera recorder for MuJoCo simulator +Subscribes to ZMQ camera feed and saves images to disk +""" +import argparse +import sys +import time +from pathlib import Path + +# Add sim module to path +sys.path.insert(0, str(Path(__file__).parent)) + +import cv2 +import numpy as np +from sim.sensor_utils import SensorClient + + +def main(): + parser = argparse.ArgumentParser(description="Save camera streams from MuJoCo simulator") + parser.add_argument("--host", type=str, default="localhost", + help="Simulator host address (default: localhost)") + parser.add_argument("--port", type=int, default=5555, + help="ZMQ port (default: 5555)") + parser.add_argument("--save-dir", type=str, default="./camera_recordings", + help="Directory to save images (default: ./camera_recordings)") + parser.add_argument("--save-rate", type=int, default=5, + help="Save every Nth frame (default: 5 = ~6Hz at 30fps stream)") + parser.add_argument("--max-frames", type=int, default=None, + help="Maximum frames to save before exiting (default: unlimited)") + args = parser.parse_args() + + # Create save directory + save_dir = Path(args.save_dir) + save_dir.mkdir(parents=True, exist_ok=True) + + print("="*60) + print("📷 MuJoCo Camera Recorder") + print("="*60) + print(f"🌐 Connecting to: tcp://{args.host}:{args.port}") + print(f"💾 Saving to: {save_dir.absolute()}") + print(f"⏬ Save rate: Every {args.save_rate} frames") + if args.max_frames: + print(f"🎬 Max frames: {args.max_frames}") + print("="*60) + print("\nWaiting for camera data...") + print("Press Ctrl+C to stop recording\n") + + # Connect to sensor server + client = SensorClient() + client.start_client(server_ip=args.host, port=args.port) + + frame_count = 0 + saved_count = 0 + last_time = time.time() + fps_display = 0 + + # Track per-camera frame counts + camera_frame_counts = {} + + try: + while True: + try: + # Receive image data + data = client.receive_message() + + # Calculate FPS + frame_count += 1 + current_time = time.time() + if current_time - last_time >= 1.0: + fps_display = frame_count / (current_time - last_time) + frame_count = 0 + last_time = current_time + + # Process each camera + for key, value in data.items(): + if key == "timestamps": + continue + + # Initialize counter for this camera + if key not in camera_frame_counts: + camera_frame_counts[key] = 0 + # Create subdirectory for each camera + (save_dir / key).mkdir(exist_ok=True) + + camera_frame_counts[key] += 1 + + # Only save every Nth frame + if camera_frame_counts[key] % args.save_rate != 0: + continue + + # Decode image if it's a string (base64 encoded) + if isinstance(value, str): + from sim.sensor_utils import ImageUtils + img = ImageUtils.decode_image(value) + elif isinstance(value, np.ndarray): + img = value + else: + continue + + # Save image + filename = f"{key}_{saved_count:06d}.jpg" + save_path = save_dir / key / filename + cv2.imwrite(str(save_path), img) + + # Print progress + if saved_count % 10 == 0: + print(f"[{fps_display:5.1f} FPS] Saved {saved_count:4d} frames to {key}/") + + saved_count += 1 + + # Check if we've reached max frames + if args.max_frames and saved_count >= args.max_frames: + print(f"\n✓ Reached max frames ({args.max_frames}). Stopping...") + return + + except KeyboardInterrupt: + raise + except Exception as e: + print(f"Error receiving/saving frame: {e}") + time.sleep(0.1) + + except KeyboardInterrupt: + print(f"\n\n✓ Recording stopped") + print(f"📊 Total frames saved: {saved_count}") + print(f"📁 Location: {save_dir.absolute()}") + finally: + client.stop_client() + + +if __name__ == "__main__": + main() + diff --git a/mujoco_sim_g1/view_cameras_live.py b/mujoco_sim_g1/view_cameras_live.py new file mode 100644 index 000000000..bacb637ab --- /dev/null +++ b/mujoco_sim_g1/view_cameras_live.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +""" +Live camera viewer for MuJoCo simulator using matplotlib +Works without X11/GTK - suitable for SSH sessions with X forwarding +""" +import argparse +import sys +import time +from pathlib import Path + +# Add sim module to path +sys.path.insert(0, str(Path(__file__).parent)) + +import cv2 +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +from sim.sensor_utils import SensorClient, ImageUtils + + +class CameraViewer: + def __init__(self, host, port): + self.client = SensorClient() + self.client.start_client(server_ip=host, port=port) + + self.fig = None + self.axes = {} + self.images = {} + self.text_objs = {} + + self.frame_count = 0 + self.last_time = time.time() + self.fps = 0 + + def init_plot(self): + """Initialize matplotlib figure and axes""" + # Wait for first frame to know how many cameras we have + print("Waiting for first frame to detect cameras...") + data = self.client.receive_message() + + # Parse camera names - handle nested 'images' dict + camera_names = [] + if "images" in data and isinstance(data["images"], dict): + # Nested structure: data["images"]["camera_name"] + camera_names = list(data["images"].keys()) + else: + # Flat structure: data["camera_name"] directly + camera_names = [k for k in data.keys() if k not in ["timestamps", "images"]] + + num_cameras = len(camera_names) + + if num_cameras == 0: + print("No cameras found in stream!") + return False + + print(f"Found {num_cameras} camera(s): {', '.join(camera_names)}") + + # Create subplots + if num_cameras == 1: + self.fig, ax = plt.subplots(1, 1, figsize=(10, 8)) + axes_list = [ax] + elif num_cameras == 2: + self.fig, axes_list = plt.subplots(1, 2, figsize=(16, 6)) + else: + rows = (num_cameras + 1) // 2 + self.fig, axes_list = plt.subplots(rows, 2, figsize=(16, 6 * rows)) + axes_list = axes_list.flatten() + + # Initialize each camera subplot + for i, cam_name in enumerate(camera_names): + ax = axes_list[i] + ax.set_title(f"{cam_name}", fontsize=12, fontweight='bold') + ax.axis('off') + + # Get image data from nested or flat structure + if "images" in data and cam_name in data["images"]: + img_data = data["images"][cam_name] + elif cam_name in data: + img_data = data[cam_name] + else: + img_data = cam_name # Use the actual data if it's the value + + # Decode first image + if isinstance(img_data, str): + img = ImageUtils.decode_image(img_data) + elif isinstance(img_data, np.ndarray): + img = img_data + else: + print(f"Warning: Unknown image format for {cam_name}: {type(img_data)}") + continue + + # Check if image is valid + if img is None or not isinstance(img, np.ndarray): + print(f"Warning: Invalid image data for {cam_name}") + continue + + # Convert BGR to RGB for matplotlib + img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Display image + im = ax.imshow(img_rgb) + self.images[cam_name] = im + self.axes[cam_name] = ax + + # Add FPS text + text = ax.text(0.02, 0.98, 'FPS: 0.0', + transform=ax.transAxes, + fontsize=10, + verticalalignment='top', + bbox=dict(boxstyle='round', facecolor='black', alpha=0.7), + color='lime', + fontweight='bold') + self.text_objs[cam_name] = text + + # Hide unused subplots + if num_cameras < len(axes_list): + for i in range(num_cameras, len(axes_list)): + axes_list[i].axis('off') + + self.fig.tight_layout() + return True + + def update_frame(self, frame_num): + """Update function for animation""" + try: + # Receive new frame + data = self.client.receive_message() + + # Calculate FPS + self.frame_count += 1 + current_time = time.time() + if current_time - self.last_time >= 1.0: + self.fps = self.frame_count / (current_time - self.last_time) + self.frame_count = 0 + self.last_time = current_time + + # Update each camera + for cam_name in self.images.keys(): + # Get image data from nested or flat structure + if "images" in data and cam_name in data["images"]: + img_data = data["images"][cam_name] + elif cam_name in data: + img_data = data[cam_name] + else: + continue + + # Decode image + if isinstance(img_data, str): + img = ImageUtils.decode_image(img_data) + elif isinstance(img_data, np.ndarray): + img = img_data + else: + continue + + # Check if image is valid + if img is None or not isinstance(img, np.ndarray): + continue + + # Convert BGR to RGB for matplotlib + img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Update image + self.images[cam_name].set_data(img_rgb) + + # Update FPS text + self.text_objs[cam_name].set_text(f'FPS: {self.fps:.1f}') + + except Exception as e: + print(f"Error updating frame: {e}") + + return list(self.images.values()) + list(self.text_objs.values()) + + def start(self, interval=33): + """Start the live viewer""" + if not self.init_plot(): + return + + print(f"\n{'='*60}") + print("📹 Live camera viewer started!") + print("Close the window or press Ctrl+C to exit") + print(f"{'='*60}\n") + + # Create animation + anim = FuncAnimation( + self.fig, + self.update_frame, + interval=interval, # ms between frames + blit=True, + cache_frame_data=False + ) + + try: + plt.show() + except KeyboardInterrupt: + print("\nStopping viewer...") + finally: + self.client.stop_client() + plt.close('all') + + +def main(): + parser = argparse.ArgumentParser(description="Live camera viewer for MuJoCo simulator") + parser.add_argument("--host", type=str, default="localhost", + help="Simulator host address (default: localhost)") + parser.add_argument("--port", type=int, default=5555, + help="ZMQ port (default: 5555)") + parser.add_argument("--interval", type=int, default=33, + help="Update interval in ms (default: 33 = ~30fps)") + args = parser.parse_args() + + print("="*60) + print("📷 MuJoCo Live Camera Viewer (matplotlib)") + print("="*60) + print(f"🌐 Connecting to: tcp://{args.host}:{args.port}") + print(f"⏱️ Update interval: {args.interval}ms (~{1000/args.interval:.0f} fps)") + print("="*60) + + viewer = CameraViewer(host=args.host, port=args.port) + viewer.start(interval=args.interval) + + +if __name__ == "__main__": + main() + diff --git a/out.wav b/out.wav new file mode 100644 index 000000000..7cc685782 Binary files /dev/null and b/out.wav differ diff --git a/replay_working.py b/replay_working.py new file mode 100644 index 000000000..9cc35efac --- /dev/null +++ b/replay_working.py @@ -0,0 +1,90 @@ +"""' +Refer to: lerobot/lerobot/scripts/eval.py + lerobot/lerobot/scripts/econtrol_robot.py + lerobot/robot_devices/control_utils.py +""" + +import time +import numpy as np +import cv2 +from multiprocessing.sharedctypes import SynchronizedArray +from lerobot.configs import parser +from lerobot.datasets.lerobot_dataset import LeRobotDataset +from lerobot.robots.unitree_g1.eval_robot.make_robot import ( + setup_image_client, + setup_robot_interface, + process_images_and_observations, +) +from lerobot.robots.unitree_g1.eval_robot.utils.utils import cleanup_resources, EvalRealConfig + +from lerobot.robots.unitree_g1.eval_robot.utils.rerun_visualizer import RerunLogger, visualization_data +from lerobot.robots.unitree_g1.eval_robot.utils.utils import to_list, to_scalar +from lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_test import ( + G1_29_ArmController, G1_29_JointIndex +) +import logging_mp +from lerobot.robots.unitree_g1.eval_robot.robot_control.robot_arm_ik import G1_29_ArmIK + +logging_mp.basic_config(level=logging_mp.INFO) +logger_mp = logging_mp.get_logger(__name__) + + +def replay_main(): + + #damp needs to be on? do i start the robot as well + + arm_ik = G1_29_ArmIK() + arm_ctrl = G1_29_ArmController(motion_mode=False, simulation_mode=False)#motors move here upon init. there's a bug where when closing python the motors die + #arm_ctrl.ctrl_dual_arm_go_home() + dataset = LeRobotDataset(repo_id='nepyope/unitree_box_push_30fps_actions_fix', root="", episodes=[0]) + actions = dataset.hf_dataset.select_columns("action") + print(actions) + episode_idx = 0 + episode_info = dataset.meta.episodes[episode_idx] + + from_idx = episode_info["dataset_from_index"] + to_idx = episode_info["dataset_to_index"] + step = dataset[from_idx] + init_left_arm_pose = step["observation.state"][:14].cpu().numpy() + print(init_left_arm_pose) + exit() + tau = arm_ik.solve_tau(init_left_arm_pose) + #arm_ctrl.ctrl_dual_arm(init_left_arm_pose, tau) + print('ok') + + # Create config object for image client + import argparse + cfg = argparse.Namespace( + sim=False, # Real robot (not simulation) + arm="G1_29", + ee="dex3", + motion=False + ) + + #replay actions from the dataset + for idx in range(dataset.num_frames): + + arm_joint_indices = set(range(15, 29)) # 15–28 are arms + for jid in G1_29_JointIndex: + if jid.value not in arm_joint_indices: + arm_ctrl.msg.motor_cmd[jid].mode = 1 + arm_ctrl.msg.motor_cmd[jid].q = 0.0 + arm_ctrl.msg.motor_cmd[jid].dq = 0.0 + arm_ctrl.msg.motor_cmd[jid].tau = 0.0 + loop_start_time = time.perf_counter() + + action_np = actions[idx]["action"].numpy() + + # exec action + arm_action = action_np[:14] + tau = arm_ik.solve_tau(arm_action) + arm_ctrl.ctrl_dual_arm(arm_action, tau) + logger_mp.info(f"arm_action {arm_action}, tau {tau}") + + # Maintain frequency + time.sleep(max(0, (1.0 / 30) - (time.perf_counter() - loop_start_time))) + + #some thread issue idk motion_mode true gets rid of the shakes motion mode + +if __name__ == "__main__": + replay_main() diff --git a/src/lerobot/robots/unitree_g1/assets/g1/locomotion/2000_exported.pt b/src/lerobot/robots/unitree_g1/assets/g1/locomotion/2000_exported.pt new file mode 100644 index 000000000..9d739e3e4 Binary files /dev/null and b/src/lerobot/robots/unitree_g1/assets/g1/locomotion/2000_exported.pt differ diff --git a/src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Balance.onnx b/src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Balance.onnx new file mode 100644 index 000000000..45161cb20 Binary files /dev/null and b/src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Balance.onnx differ diff --git a/src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Walk-converted.onnx b/src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Walk-converted.onnx new file mode 100644 index 000000000..fb7db7467 Binary files /dev/null and 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+joint_ids_map: [0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 22, 4, 10, 16, 23, 5, 11, + 17, 24, 18, 25, 19, 26, 20, 27, 21, 28] +step_dt: 0.02 +stiffness: [40.2, 99.1, 40.2, 99.1, 28.5, 28.5, 40.2, 99.1, 40.2, 99.1, 28.5, 28.5, + 40.2, 28.5, 28.5, 14.3, 14.3, 14.3, 14.3, 14.3, 16.8, 16.8, 14.3, 14.3, 14.3, 14.3, + 14.3, 16.8, 16.8] +damping: [2.56, 6.31, 2.56, 6.31, 1.81, 1.81, 2.56, 6.31, 2.56, 6.31, 1.81, 1.81, + 2.56, 1.81, 1.81, 0.907, 0.907, 0.907, 0.907, 0.907, 1.07, 1.07, 0.907, 0.907, 0.907, + 0.907, 0.907, 1.07, 1.07] +default_joint_pos: [-0.302, -0.319, 0.00124, 0.000442, 0.00489, 0.00191, 0.00929, + 0.00796, 0.00546, 0.672, 0.67, 0.2, 0.202, -0.368, -0.355, 0.194, -0.196, -0.00644, + 0.00976, 0.00258, -0.00029, 0.605, 0.596, 0.00818, 0.00322, 0.00293, -0.00339, -0.00955, + -0.00715] +commands: {} +actions: + JointPositionAction: + clip: null + joint_names: [.*] + scale: [0.548, 0.548, 0.548, 0.351, 0.351, 0.439, 0.548, 0.548, 0.439, 0.351, + 0.351, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, + 0.439, 0.439, 0.439, 0.439, 0.0745, 0.0745, 0.0745, 0.0745] + offset: [-0.302, -0.319, 0.00124, 0.000442, 0.00489, 0.00191, 0.00929, 0.00796, + 0.00546, 0.672, 0.67, 0.2, 0.202, -0.368, -0.355, 0.194, -0.196, -0.00644, 0.00976, + 0.00258, -0.00029, 0.605, 0.596, 0.00818, 0.00322, 0.00293, -0.00339, -0.00955, + -0.00715] + joint_ids: null +observations: + motion_command: + params: {command_name: motion} + clip: null + scale: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + history_length: 1 + motion_anchor_ori_b: + params: {command_name: motion} + clip: null + scale: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + history_length: 1 + base_ang_vel: + params: {} + clip: null + scale: [1.0, 1.0, 1.0] + history_length: 1 + joint_pos_rel: + params: {} + clip: null + scale: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + history_length: 1 + joint_vel_rel: + params: {} + clip: null + scale: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + history_length: 1 + last_action: + params: {} + clip: null + scale: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + history_length: 1 diff --git a/src/lerobot/robots/unitree_g1/assets/g1/mimic/gangnam_style/policy.onnx b/src/lerobot/robots/unitree_g1/assets/g1/mimic/gangnam_style/policy.onnx new file mode 100644 index 000000000..6cb0d310f Binary files /dev/null and b/src/lerobot/robots/unitree_g1/assets/g1/mimic/gangnam_style/policy.onnx differ diff --git a/src/lerobot/robots/unitree_g1/config_unitree_g1.py b/src/lerobot/robots/unitree_g1/config_unitree_g1.py index c4b2ef354..47e467f76 100644 --- a/src/lerobot/robots/unitree_g1/config_unitree_g1.py +++ b/src/lerobot/robots/unitree_g1/config_unitree_g1.py @@ -41,14 +41,63 @@ class UnitreeG1Config(RobotConfig): arm_velocity_limit = 100.0 control_dt = 1.0 / 250.0 - speed_gradual_max = False - gradual_start_time = None - gradual_time = None + speed_gradual_max: bool = False + gradual_start_time: float | None = None + gradual_time: float | None = None - audio_client = True + audio_client: bool = True - freeze_body = False - - gravity_compensation = False + freeze_body: bool = False + gravity_compensation: bool = False cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Locomotion control + locomotion_control: bool = False + #policy_path: str = "src/lerobot/robots/unitree_g1/assets/g1/locomotion/motion.pt" + policy_path: str = "src/lerobot/robots/unitree_g1/assets/g1/locomotion/GR00T-WholeBodyControl-Walk.onnx" + + # Motion imitation (dance_102, gangnam_style, etc) + motion_imitation_control: bool = False + motion_policy_path: str = "unitree_rl_lab/deploy/robots/g1_29dof/config/policy/mimic/dance_102/exported/policy.onnx" # Use policy + #motion_policy_path: str = None # Set to None for direct playback mode (no policy) + motion_file_path: str = "unitree_rl_lab/deploy/robots/g1_29dof/config/policy/mimic/dance_102/params/G1_Take_102.bvh_60hz.csv" + motion_fps: float = 60.0 + motion_control_dt: float = 0.02 + + # Motion imitation parameters (from deploy.yaml) + + motion_joint_ids_map: list = field(default_factory=lambda: [0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 22, 4, 10, 16, 23, 5, 11, 17, 24, 18, 25, 19, 26, 20, 27, 21, 28]) + motion_stiffness: list = field(default_factory=lambda: [40.2, 99.1, 40.2, 99.1, 28.5, 28.5, 40.2, 99.1, 40.2, 99.1, 28.5, 28.5, 40.2, 28.5, 28.5, 14.3, 14.3, 14.3, 14.3, 14.3, 16.8, 16.8, 14.3, 14.3, 14.3, 14.3, 14.3, 16.8, 16.8]) + motion_damping: list = field(default_factory=lambda: [2.56, 6.31, 2.56, 6.31, 1.81, 1.81, 2.56, 6.31, 2.56, 6.31, 1.81, 1.81, 2.56, 1.81, 1.81, 0.907, 0.907, 0.907, 0.907, 0.907, 1.07, 1.07, 0.907, 0.907, 0.907, 0.907, 0.907, 1.07, 1.07]) + motion_default_joint_pos: list = field(default_factory=lambda: [-0.302, -0.319, 0.00124, 0.000442, 0.00489, 0.00191, 0.00929, 0.00796, 0.00546, 0.672, 0.67, 0.2, 0.202, -0.368, -0.355, 0.194, -0.196, -0.00644, 0.00976, 0.00258, -0.00029, 0.605, 0.596, 0.00818, 0.00322, 0.00293, -0.00339, -0.00955, -0.00715]) + motion_action_scale: list = field(default_factory=lambda: [0.548, 0.548, 0.548, 0.351, 0.351, 0.439, 0.548, 0.548, 0.439, 0.351, 0.351, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.439, 0.0745, 0.0745, 0.0745, 0.0745]) + + # Locomotion parameters (from g1.yaml) + locomotion_control_dt: float = 0.02 + + leg_joint2motor_idx: list = field(default_factory=lambda: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) + locomotion_kps: list = field(default_factory=lambda: [150, 150, 150, 300, 40, 40, 150, 150, 150, 300, 40, 40]) + locomotion_kds: list = field(default_factory=lambda: [2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 2, 2]) + default_leg_angles: list = field(default_factory=lambda: [-0.1, 0.0, 0.0, 0.3, -0.2, 0.0, -0.1, 0.0, 0.0, 0.3, -0.2, 0.0]) + + arm_waist_joint2motor_idx: list = field(default_factory=lambda: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]) + locomotion_arm_waist_kps: list = field(default_factory=lambda: [250, 250, 250, 100, 100, 50, 50, 20, 20, 20, 100, 100, 50, 50, 20, 20, 20]) + locomotion_arm_waist_kds: list = field(default_factory=lambda: [5, 5, 5, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1]) + locomotion_arm_waist_target: list = field(default_factory=lambda: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + + ang_vel_scale: float = 0.25 + dof_pos_scale: float = 1.0 + dof_vel_scale: float = 0.05 + locomotion_action_scale: float = 0.25 + cmd_scale: list = field(default_factory=lambda: [2.0, 2.0, 0.25]) + + # GR00T-specific scaling (different from regular locomotion!) + groot_ang_vel_scale: float = 0.25 # GR00T uses 0.5, not 0.25 + groot_cmd_scale: list = field(default_factory=lambda: [2.0, 2.0, 0.25]) # yaw is 0.5 for GR00T + num_locomotion_actions: int = 12 + num_locomotion_obs: int = 47 + max_cmd: list = field(default_factory=lambda: [0.8, 0.5, 1.57]) + locomotion_imu_type: str = "pelvis" # "torso" or "pelvis" + + diff --git a/src/lerobot/robots/unitree_g1/unitree_g1.py b/src/lerobot/robots/unitree_g1/unitree_g1.py index fd43a0c80..101756c97 100644 --- a/src/lerobot/robots/unitree_g1/unitree_g1.py +++ b/src/lerobot/robots/unitree_g1/unitree_g1.py @@ -21,6 +21,11 @@ import numpy as np import threading import time from enum import IntEnum +import sys +import select +import termios +import tty +from collections import deque from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize # dds from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as hg_LowCmd, LowState_ as hg_LowState # idl for g1, h1_2 @@ -31,10 +36,39 @@ from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowCmd_ as go_LowCmd, LowState_ as go_LowState # idl for h1 from unitree_sdk2py.idl.default import unitree_go_msg_dds__LowCmd_ + +from typing import Union +import numpy as np +import time +import torch +import onnxruntime as ort + +from unitree_sdk2py.core.channel import ChannelPublisher, ChannelFactoryInitialize +from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize +from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_, unitree_hg_msg_dds__LowState_ +from unitree_sdk2py.idl.default import unitree_go_msg_dds__LowCmd_, unitree_go_msg_dds__LowState_ +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_ as LowCmdHG +from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowCmd_ as LowCmdGo +from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_ as LowStateHG +from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowState_ as LowStateGo +from unitree_sdk2py.utils.crc import CRC +from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import ( + MotionSwitcherClient, +) + +from scipy.spatial.transform import Rotation as R + +import struct + +import yaml + +from typing import Union + import logging_mp from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK +import torch logger_mp = logging_mp.get_logger(__name__) @@ -69,6 +103,7 @@ class G1_29_LowState: def __init__(self): self.motor_state = [MotorState() for _ in range(G1_29_Num_Motors)] self.imu_state = IMUState() + self.wireless_remote = None # Raw wireless remote data class DataBuffer: def __init__(self): @@ -127,12 +162,29 @@ class UnitreeG1(Robot): self.arm_ik = G1_29_ArmIK() + # initialize lowcmd publisher and lowstate subscriber if self.simulation_mode: - ChannelFactoryInitialize(1) + ChannelFactoryInitialize(0, "lo") else: ChannelFactoryInitialize(0) + + if not self.config.simulation_mode: + self.msc = MotionSwitcherClient() + self.msc.SetTimeout(5.0) + self.msc.Init() + + status, result = self.msc.CheckMode() + print(status, result) + #check if result name first + if result is not None and "name" in result: + while result["name"]: + self.msc.ReleaseMode() + status, result = self.msc.CheckMode() + print(status, result) + time.sleep(1) + if self.motion_mode: self.lowcmd_publisher = ChannelPublisher(kTopicLowCommand_Motion, hg_LowCmd) else: @@ -188,6 +240,7 @@ class UnitreeG1(Robot): #print current motor q, kp, kd + if config.audio_client: self.audio_client = AudioClient() self.audio_client.SetTimeout(10.0) @@ -198,11 +251,124 @@ class UnitreeG1(Robot): # for i in range(10000): # print(self.get_current_motor_q()) # time.sleep(0.05) - # initialize publish thread - self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + + # Initialize control flags BEFORE starting threads + self.keyboard_thread = None + self.keyboard_running = False + self.locomotion_thread = None + self.locomotion_running = False + self.motion_imitation_thread = None + self.motion_imitation_running = False + + # Initialize publish thread ONLY if not using motion imitation or locomotion + # (those modes handle their own motor commands and publishing) + self.publish_thread = None self.ctrl_lock = threading.Lock() - self.publish_thread.daemon = True - self.publish_thread.start() + if not config.motion_imitation_control and not config.locomotion_control: + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.publish_thread.daemon = True + self.publish_thread.start() + logger_mp.info("Arm control publish thread started") + + # Load locomotion policy if enabled + self.policy = None + self.policy_type = None # 'torchscript', 'onnx', or 'motion_imitation' + self.motion_loader = None + + if config.motion_imitation_control: + # Motion imitation mode (dance, etc.) + if config.motion_file_path is None: + raise ValueError("motion_imitation_control is True but motion_file_path is not set") + + logger_mp.info(f"Loading motion reference from {config.motion_file_path}") + + # Load motion file + self.motion_loader = self.MotionLoader(config.motion_file_path, config.motion_fps) + + # Load ONNX policy (optional for now - can run in direct playback mode) + if config.motion_policy_path and Path(config.motion_policy_path).exists(): + logger_mp.info(f"Loading motion imitation policy from {config.motion_policy_path}") + self.policy = ort.InferenceSession(config.motion_policy_path) + self.policy_type = 'motion_imitation' + logger_mp.info("Motion imitation ONNX policy loaded successfully") + logger_mp.info(f"ONNX input: {self.policy.get_inputs()[0].name}, shape: {self.policy.get_inputs()[0].shape}") + logger_mp.info(f"ONNX output: {self.policy.get_outputs()[0].name}, shape: {self.policy.get_outputs()[0].shape}") + else: + logger_mp.info("Running in DIRECT PLAYBACK mode (no policy - just reference motion)") + self.policy = None + self.policy_type = 'motion_playback' + + # Initialize motion imitation variables + self.motion_counter = 0 + self.motion_qj_all = np.zeros(29, dtype=np.float32) # All 29 joints from robot + self.motion_dqj_all = np.zeros(29, dtype=np.float32) + self.motion_action = np.zeros(29, dtype=np.float32) # 29D action output + self.motion_obs = np.zeros(154, dtype=np.float32) # 154D observation + self.motion_elapsed_time = 0.0 + + # Initialize motion and start + self.init_motion_imitation() + + elif config.locomotion_control: + if config.policy_path is None: + raise ValueError("locomotion_control is True but policy_path is not set") + + logger_mp.info(f"Loading locomotion policy from {config.policy_path}") + + # Check file extension and load accordingly + if config.policy_path.endswith('.pt'): + logger_mp.info("Detected TorchScript (.pt) policy") + self.policy = torch.jit.load(config.policy_path) + self.policy_type = 'torchscript' + logger_mp.info("TorchScript policy loaded successfully") + elif config.policy_path.endswith('.onnx'): + logger_mp.info("Detected ONNX (.onnx) policy") + self.policy = ort.InferenceSession(config.policy_path) + self.policy_type = 'onnx' + logger_mp.info("ONNX policy loaded successfully") + logger_mp.info(f"ONNX input: {self.policy.get_inputs()[0].name}, shape: {self.policy.get_inputs()[0].shape}") + logger_mp.info(f"ONNX output: {self.policy.get_outputs()[0].name}, shape: {self.policy.get_outputs()[0].shape}") + else: + raise ValueError(f"Unsupported policy format: {config.policy_path}. Only .pt (TorchScript) and .onnx (ONNX) are supported.") + + # Initialize locomotion variables + self.remote_controller = self.RemoteController() + self.locomotion_counter = 0 + self.qj = np.zeros(config.num_locomotion_actions, dtype=np.float32) + self.dqj = np.zeros(config.num_locomotion_actions, dtype=np.float32) + self.locomotion_action = np.zeros(config.num_locomotion_actions, dtype=np.float32) + self.locomotion_obs = np.zeros(config.num_locomotion_obs, dtype=np.float32) + self.locomotion_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32) + + # GR00T-specific variables (for ONNX policies with 29 joints) + if self.policy_type == 'onnx': + self.groot_qj_all = np.zeros(29, dtype=np.float32) # All 29 joints + self.groot_dqj_all = np.zeros(29, dtype=np.float32) + self.groot_action = np.zeros(15, dtype=np.float32) # 15D action (legs + waist) + self.groot_obs_single = np.zeros(86, dtype=np.float32) # 86D single frame observation + self.groot_obs_history = deque(maxlen=6) # 6-frame history buffer + self.groot_obs_stacked = np.zeros(516, dtype=np.float32) # 86D × 6 = 516D stacked observation + self.groot_height_cmd = 0.74 # Default base height + self.groot_orientation_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32) # roll, pitch, yaw + + # Initialize history with zeros + for _ in range(6): + self.groot_obs_history.append(np.zeros(86, dtype=np.float32)) + + # Start keyboard controls if in simulation mode + if self.simulation_mode: + logger_mp.info("Starting keyboard controls for simulation...") + self.start_keyboard_controls() + + # Use different init based on policy type + if self.policy_type == 'onnx': + self.init_groot_locomotion() + else: + self.init_locomotion() + elif self.simulation_mode: + # Even without locomotion, provide keyboard feedback in sim + logger_mp.info("Simulation mode active (locomotion disabled)") + logger_mp.info("Initialize G1 OK!\n") @@ -227,6 +393,9 @@ class UnitreeG1(Robot): lowstate.imu_state.rpy = list(msg.imu_state.rpy) lowstate.imu_state.temperature = msg.imu_state.temperature + # Capture wireless remote data + lowstate.wireless_remote = msg.wireless_remote + self.lowstate_buffer.SetData(lowstate) current_time = time.time() @@ -243,11 +412,15 @@ class UnitreeG1(Robot): return cliped_arm_q_target def _ctrl_motor_state(self): + """Arm control thread - publishes commands for arms only. + NOTE: This thread is NOT started when motion_imitation_control or locomotion_control is True. + Those modes handle their own publishing.""" if self.motion_mode: self.msg.motor_cmd[G1_29_JointIndex.kNotUsedJoint0].q = 1.0 while True: start_time = time.time() + with self.ctrl_lock: arm_q_target = self.q_target arm_tauff_target = self.tauff_target @@ -262,6 +435,17 @@ class UnitreeG1(Robot): self.msg.motor_cmd[id].dq = 0 self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + # Zero out specific joints when in simulation mode + if self.simulation_mode: + # Waist joints + self.msg.motor_cmd[G1_29_JointIndex.kWaistYaw].q = 0.0 + self.msg.motor_cmd[G1_29_JointIndex.kWaistPitch].q = 0.0 + # Wrist joints + self.msg.motor_cmd[G1_29_JointIndex.kLeftWristPitch].q = 0.0 + self.msg.motor_cmd[G1_29_JointIndex.kLeftWristyaw].q = 0.0 + self.msg.motor_cmd[G1_29_JointIndex.kRightWristPitch].q = 0.0 + self.msg.motor_cmd[G1_29_JointIndex.kRightWristYaw].q = 0.0 + self.msg.crc = self.crc.Crc(self.msg) self.lowcmd_publisher.Write(self.msg) @@ -684,6 +868,1007 @@ class UnitreeG1(Robot): return calibrated + ###################LOCOMOTION CONTROL################### + + def locomotion_create_damping_cmd(self): + """Set all motors to damping mode (kp=0, kd=8).""" + size = len(self.msg.motor_cmd) + for i in range(size): + self.msg.motor_cmd[i].q = 0 + self.msg.motor_cmd[i].qd = 0 + self.msg.motor_cmd[i].kp = 0 + self.msg.motor_cmd[i].kd = 8 + self.msg.motor_cmd[i].tau = 0 + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + def locomotion_create_zero_cmd(self): + """Set all motors to zero torque mode.""" + size = len(self.msg.motor_cmd) + for i in range(size): + self.msg.motor_cmd[i].q = 0 + self.msg.motor_cmd[i].qd = 0 + self.msg.motor_cmd[i].kp = 0 + self.msg.motor_cmd[i].kd = 0 + self.msg.motor_cmd[i].tau = 0 + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + def locomotion_zero_torque_state(self): + """Enter zero torque state.""" + logger_mp.info("Enter zero torque state.") + self.locomotion_create_zero_cmd() + time.sleep(self.config.locomotion_control_dt) + + def locomotion_move_to_default_pos(self): + """Move robot legs to default standing position over 2 seconds (arms are not moved).""" + logger_mp.info("Moving legs to default locomotion pos.") + total_time = 2.0 + num_step = int(total_time / self.config.locomotion_control_dt) + + # Only control legs, not arms + dof_idx = self.config.leg_joint2motor_idx + kps = self.config.locomotion_kps + kds = self.config.locomotion_kds + default_pos = np.array(self.config.default_leg_angles, dtype=np.float32) + dof_size = len(dof_idx) + + # Get current lowstate + lowstate = self.lowstate_buffer.GetData() + if lowstate is None: + logger_mp.error("Cannot get lowstate for locomotion") + return + + # Record the current leg positions + init_dof_pos = np.zeros(dof_size, dtype=np.float32) + for i in range(dof_size): + init_dof_pos[i] = lowstate.motor_state[dof_idx[i]].q + + # Move legs to default pos + for i in range(num_step): + alpha = i / num_step + for j in range(dof_size): + motor_idx = dof_idx[j] + target_pos = default_pos[j] + self.msg.motor_cmd[motor_idx].q = init_dof_pos[j] * (1 - alpha) + target_pos * alpha + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = kps[j] + self.msg.motor_cmd[motor_idx].kd = kds[j] + self.msg.motor_cmd[motor_idx].tau = 0 + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + time.sleep(self.config.locomotion_control_dt) + logger_mp.info("Reached default locomotion position (legs only)") + + def locomotion_default_pos_state(self): + """Hold default leg position for 2 seconds (arms are not controlled).""" + logger_mp.info("Enter default pos state - holding legs for 2 seconds") + + # Only control legs, not arms + for i in range(len(self.config.leg_joint2motor_idx)): + motor_idx = self.config.leg_joint2motor_idx[i] + self.msg.motor_cmd[motor_idx].q = self.config.default_leg_angles[i] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.locomotion_kps[i] + self.msg.motor_cmd[motor_idx].kd = self.config.locomotion_kds[i] + self.msg.motor_cmd[motor_idx].tau = 0 + + # Hold leg position for 2 seconds + hold_time = 2.0 + num_steps = int(hold_time / self.config.locomotion_control_dt) + for _ in range(num_steps): + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + time.sleep(self.config.locomotion_control_dt) + logger_mp.info("Finished holding default leg position") + + + class RemoteController: + def __init__(self): + self.lx = 0 + self.ly = 0 + self.rx = 0 + self.ry = 0 + self.button = [0] * 16 + + def set(self, data): + # wireless_remote + keys = struct.unpack("H", data[2:4])[0] + for i in range(16): + self.button[i] = (keys & (1 << i)) >> i + self.lx = struct.unpack("f", data[4:8])[0] + self.rx = struct.unpack("f", data[8:12])[0] + self.ry = struct.unpack("f", data[12:16])[0] + self.ly = struct.unpack("f", data[20:24])[0] + + class MotionLoader: + """Load and interpolate motion from CSV file for motion imitation.""" + def __init__(self, motion_file: str, fps: float = 60.0): + """Load motion from CSV file. + + CSV format: [root_pos(3), root_quat_xyzw(4), joint_dof(29)] per row + """ + self.dt = 1.0 / fps + + # Load CSV + data = np.loadtxt(motion_file, delimiter=',') + self.num_frames = data.shape[0] + self.duration = self.num_frames * self.dt + + # Split data + self.root_positions = data[:, 0:3] # (N, 3) + self.root_quaternions_xyzw = data[:, 3:7] # (N, 4) [x, y, z, w] + self.dof_positions = data[:, 7:] # (N, 29) + + # Compute velocities (finite differences) + self.dof_velocities = np.diff(self.dof_positions, axis=0, prepend=self.dof_positions[0:1]) / self.dt + + # Current playback state + self.current_time = 0.0 + self.index_0 = 0 + self.index_1 = 0 + self.blend = 0.0 + + logger_mp.info(f"MotionLoader: Loaded {self.num_frames} frames, duration={self.duration:.2f}s") + + def update(self, time: float): + """Update motion to specific time (loops at duration).""" + self.current_time = time % self.duration # Loop + phase = self.current_time / self.duration + + self.index_0 = int(phase * (self.num_frames - 1)) + self.index_1 = min(self.index_0 + 1, self.num_frames - 1) + self.blend = (self.current_time - self.index_0 * self.dt) / self.dt + + def get_joint_pos(self) -> np.ndarray: + """Get interpolated joint positions (29D).""" + return self.dof_positions[self.index_0] * (1 - self.blend) + \ + self.dof_positions[self.index_1] * self.blend + + def get_joint_vel(self) -> np.ndarray: + """Get interpolated joint velocities (29D).""" + return self.dof_velocities[self.index_0] * (1 - self.blend) + \ + self.dof_velocities[self.index_1] * self.blend + + def get_root_quat_wxyz(self) -> np.ndarray: + """Get interpolated root quaternion [w, x, y, z].""" + # Spherical linear interpolation (SLERP) + q0 = self.root_quaternions_xyzw[self.index_0] # [x, y, z, w] + q1 = self.root_quaternions_xyzw[self.index_1] + + # Convert to scipy format [x, y, z, w] + r0 = R.from_quat(q0) + r1 = R.from_quat(q1) + + # SLERP + key_times = [0, 1] + key_rots = R.from_quat([q0, q1]) + slerp = R.from_quat(key_rots.as_quat()) # Simplified - just use linear for now + + # Linear interpolation for simplicity + quat_xyzw = q0 * (1 - self.blend) + q1 * self.blend + # Normalize + quat_xyzw = quat_xyzw / np.linalg.norm(quat_xyzw) + + # Convert to [w, x, y, z] + return np.array([quat_xyzw[3], quat_xyzw[0], quat_xyzw[1], quat_xyzw[2]], dtype=np.float32) + + def locomotion_get_gravity_orientation(self, quaternion): + """Get gravity orientation from quaternion.""" + qw = quaternion[0] + qx = quaternion[1] + qy = quaternion[2] + qz = quaternion[3] + + gravity_orientation = np.zeros(3) + gravity_orientation[0] = 2 * (-qz * qx + qw * qy) + gravity_orientation[1] = -2 * (qz * qy + qw * qx) + gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz) + + return gravity_orientation + + def locomotion_transform_imu_data(self, waist_yaw, waist_yaw_omega, imu_quat, imu_omega): + """Transform IMU data from torso to pelvis frame.""" + RzWaist = R.from_euler("z", waist_yaw).as_matrix() + R_torso = R.from_quat([imu_quat[1], imu_quat[2], imu_quat[3], imu_quat[0]]).as_matrix() + R_pelvis = np.dot(R_torso, RzWaist.T) + w = np.dot(RzWaist, imu_omega[0]) - np.array([0, 0, waist_yaw_omega]) + return R.from_matrix(R_pelvis).as_quat()[[3, 0, 1, 2]], w + + def locomotion_run(self): + """Main locomotion policy loop - runs policy and sends leg commands.""" + self.locomotion_counter += 1 + + # Get current lowstate + lowstate = self.lowstate_buffer.GetData() + if lowstate is None: + return + + # Update remote controller from lowstate + if lowstate.wireless_remote is not None: + self.remote_controller.set(lowstate.wireless_remote) + else: + # Default to zero commands if no remote data + self.remote_controller.lx = 0.0 + self.remote_controller.ly = 0.0 + self.remote_controller.rx = 0.0 + self.remote_controller.ry = 0.0 + + # Get the current joint position and velocity (LEGS ONLY) + for i in range(len(self.config.leg_joint2motor_idx)): + self.qj[i] = lowstate.motor_state[self.config.leg_joint2motor_idx[i]].q + self.dqj[i] = lowstate.motor_state[self.config.leg_joint2motor_idx[i]].dq + + # Get IMU data + quat = lowstate.imu_state.quaternion + ang_vel = np.array([lowstate.imu_state.gyroscope], dtype=np.float32) + + if self.config.locomotion_imu_type == "torso": + # Transform IMU data from torso to pelvis frame + waist_yaw = lowstate.motor_state[self.config.arm_waist_joint2motor_idx[0]].q + waist_yaw_omega = lowstate.motor_state[self.config.arm_waist_joint2motor_idx[0]].dq + quat, ang_vel = self.locomotion_transform_imu_data(waist_yaw, waist_yaw_omega, quat, ang_vel) + + # Create observation + gravity_orientation = self.locomotion_get_gravity_orientation(quat) + qj_obs = self.qj.copy() + dqj_obs = self.dqj.copy() + qj_obs = (qj_obs - np.array(self.config.default_leg_angles)) * self.config.dof_pos_scale + dqj_obs = dqj_obs * self.config.dof_vel_scale + ang_vel = ang_vel * self.config.ang_vel_scale + + # Calculate phase + period = 0.8 + count = self.locomotion_counter * self.config.locomotion_control_dt + phase = count % period / period + sin_phase = np.sin(2 * np.pi * phase) + cos_phase = np.cos(2 * np.pi * phase) + + # Get velocity commands from remote controller (only if NOT in simulation mode) + # In simulation mode, keyboard controls set self.locomotion_cmd directly + if not self.simulation_mode: + self.locomotion_cmd[0] = self.remote_controller.ly + self.locomotion_cmd[1] = self.remote_controller.lx * -1 + self.locomotion_cmd[2] = self.remote_controller.rx * -1 + + # Debug: print remote controller values every 50 iterations (~1 second at 50Hz) + if self.locomotion_counter % 50 == 0: + logger_mp.debug(f"Remote controller - lx:{self.remote_controller.lx:.2f}, ly:{self.remote_controller.ly:.2f}, rx:{self.remote_controller.rx:.2f}") + + # Build observation vector + num_actions = self.config.num_locomotion_actions + self.locomotion_obs[:3] = ang_vel + self.locomotion_obs[3:6] = gravity_orientation + self.locomotion_obs[6:9] = self.locomotion_cmd * np.array(self.config.cmd_scale) * np.array(self.config.max_cmd) + self.locomotion_obs[9 : 9 + num_actions] = qj_obs + self.locomotion_obs[9 + num_actions : 9 + num_actions * 2] = dqj_obs + self.locomotion_obs[9 + num_actions * 2 : 9 + num_actions * 3] = self.locomotion_action + self.locomotion_obs[9 + num_actions * 3] = sin_phase + self.locomotion_obs[9 + num_actions * 3 + 1] = cos_phase + + # Get action from policy network + obs_tensor = torch.from_numpy(self.locomotion_obs).unsqueeze(0) + + if self.policy_type == 'torchscript': + # TorchScript inference + self.locomotion_action = self.policy(obs_tensor).detach().numpy().squeeze() + elif self.policy_type == 'onnx': + # ONNX inference + ort_inputs = {self.policy.get_inputs()[0].name: obs_tensor.cpu().numpy()} + ort_outs = self.policy.run(None, ort_inputs) + self.locomotion_action = ort_outs[0].squeeze() + else: + raise ValueError(f"Unknown policy type: {self.policy_type}") + + # Transform action to target joint positions + target_dof_pos = np.array(self.config.default_leg_angles) + self.locomotion_action * self.config.locomotion_action_scale + + # Send commands to LEG motors only + for i in range(len(self.config.leg_joint2motor_idx)): + motor_idx = self.config.leg_joint2motor_idx[i] + self.msg.motor_cmd[motor_idx].q = target_dof_pos[i] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.locomotion_kps[i] + self.msg.motor_cmd[motor_idx].kd = self.config.locomotion_kds[i] + self.msg.motor_cmd[motor_idx].tau = 0 + + # Hold WAIST motors at 0 (indices 12, 13, 14 = WaistYaw, WaistRoll, WaistPitch) + waist_indices = self.config.arm_waist_joint2motor_idx[:3] # First 3 are waist + for i, motor_idx in enumerate(waist_indices): + self.msg.motor_cmd[motor_idx].q = 0.0 + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.locomotion_arm_waist_kps[i] + self.msg.motor_cmd[motor_idx].kd = self.config.locomotion_arm_waist_kds[i] + self.msg.motor_cmd[motor_idx].tau = 0 + + # Send command + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + def groot_locomotion_run(self): + """GR00T-style locomotion policy loop for ONNX policies - reads all 29 joints, outputs 15D action.""" + self.locomotion_counter += 1 + + # Get current lowstate + lowstate = self.lowstate_buffer.GetData() + if lowstate is None: + return + + # Update remote controller from lowstate + if lowstate.wireless_remote is not None: + self.remote_controller.set(lowstate.wireless_remote) + + # R1/R2 buttons for height control on real robot (button indices 4 and 5) + if self.remote_controller.button[0]: # R1 - raise height + self.groot_height_cmd += 0.001 # Small increment per timestep (~0.05m per second at 50Hz) + self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00) + if self.remote_controller.button[4]: # R2 - lower height + self.groot_height_cmd -= 0.001 # Small decrement per timestep + self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00) + else: + # Default to zero commands if no remote data + self.remote_controller.lx = 0.0 + self.remote_controller.ly = 0.0 + self.remote_controller.rx = 0.0 + self.remote_controller.ry = 0.0 + + # Get ALL 29 joint positions and velocities + for i in range(29): + self.groot_qj_all[i] = lowstate.motor_state[i].q + self.groot_dqj_all[i] = lowstate.motor_state[i].dq + + # Get IMU data + quat = lowstate.imu_state.quaternion + ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32) + + # Transform IMU if using torso IMU + if self.config.locomotion_imu_type == "torso": + waist_yaw = lowstate.motor_state[12].q # Waist yaw index + waist_yaw_omega = lowstate.motor_state[12].dq + quat, ang_vel_3d = self.locomotion_transform_imu_data(waist_yaw, waist_yaw_omega, quat, np.array([ang_vel])) + ang_vel = ang_vel_3d.flatten() + + # Create observation + gravity_orientation = self.locomotion_get_gravity_orientation(quat) + joints_to_zero_obs = [12, 14, 20, 21, 27, 28] # Note: NOT 13 (waist roll exists) + for idx in joints_to_zero_obs: + self.groot_qj_all[idx] = 0.0 + self.groot_dqj_all[idx] = 0.0 + # Scale joint positions and velocities + qj_obs = self.groot_qj_all.copy() + dqj_obs = self.groot_dqj_all.copy() + + # Subtract default angles for legs + waist (15 joints) + # GR00T default_angles: [-0.1, 0.0, 0.0, 0.3, -0.2, 0.0, -0.1, 0.0, 0.0, 0.3, -0.2, 0.0, 0.0, 0.0, 0.0] + groot_default_angles = np.array([-0.1, 0.0, 0.0, 0.3, -0.2, 0.0, # left leg + -0.1, 0.0, 0.0, 0.3, -0.2, 0.0, # right leg + 0.0, 0.0, 0.0, # waist + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, # left arm (zeroed) + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float32) # right arm (zeroed) + + qj_obs = (qj_obs - groot_default_angles) * self.config.dof_pos_scale + dqj_obs = dqj_obs * self.config.dof_vel_scale + ang_vel_scaled = ang_vel * self.config.groot_ang_vel_scale # Use GR00T-specific scaling! + + # Get velocity commands (keyboard or remote) + if not self.simulation_mode: + self.locomotion_cmd[0] = self.remote_controller.ly + self.locomotion_cmd[1] = self.remote_controller.lx * -1 + self.locomotion_cmd[2] = self.remote_controller.rx * -1 + + # Build 86D single frame observation (GR00T format) + self.groot_obs_single[:3] = self.locomotion_cmd * np.array(self.config.groot_cmd_scale) # cmd - use GR00T scaling! + self.groot_obs_single[3] = self.groot_height_cmd # height_cmd + self.groot_obs_single[4:7] = self.groot_orientation_cmd # roll, pitch, yaw cmd + self.groot_obs_single[7:10] = ang_vel_scaled # angular velocity + self.groot_obs_single[10:13] = gravity_orientation # gravity + self.groot_obs_single[13:42] = qj_obs # joint positions (29D) + self.groot_obs_single[42:71] = dqj_obs # joint velocities (29D) + self.groot_obs_single[71:86] = self.groot_action # previous actions (15D) + + # Add to history and stack observations (6 frames × 86D = 516D) + self.groot_obs_history.append(self.groot_obs_single.copy()) + + # Stack all 6 frames into 516D vector + for i, obs_frame in enumerate(self.groot_obs_history): + start_idx = i * 86 + end_idx = start_idx + 86 + self.groot_obs_stacked[start_idx:end_idx] = obs_frame + + # Run policy inference (ONNX) with 516D stacked observation + obs_tensor = torch.from_numpy(self.groot_obs_stacked).unsqueeze(0) + ort_inputs = {self.policy.get_inputs()[0].name: obs_tensor.cpu().numpy()} + ort_outs = self.policy.run(None, ort_inputs) + self.groot_action = ort_outs[0].squeeze() + + # Zero out waist actions (yaw=12, roll=13, pitch=14) - only use leg actions (0-11) + # This ensures action history in observations matches what's actually executed + self.groot_action[12] = 0.0 # Waist yaw + self.groot_action[13] = 0.0 # Waist roll + self.groot_action[14] = 0.0 # Waist pitch + + # Transform action to target joint positions (15D: legs + waist, but waist actions are zeroed) + target_dof_pos_15 = groot_default_angles[:15] + self.groot_action * self.config.locomotion_action_scale + + # Send commands to LEG motors (0-11) + for i in range(12): + motor_idx = i + self.msg.motor_cmd[motor_idx].q = target_dof_pos_15[i] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.locomotion_kps[i] + self.msg.motor_cmd[motor_idx].kd = self.config.locomotion_kds[i] + self.msg.motor_cmd[motor_idx].tau = 0 + + # Send WAIST commands - but SKIP waist yaw (12) and waist pitch (14) + # Only send waist roll (13) + waist_roll_idx = 13 + waist_roll_action_idx = 13 # In the 15D action + self.msg.motor_cmd[waist_roll_idx].q = target_dof_pos_15[waist_roll_action_idx] + self.msg.motor_cmd[waist_roll_idx].qd = 0 + self.msg.motor_cmd[waist_roll_idx].kp = self.config.locomotion_arm_waist_kps[1] # index 1 is waist roll + self.msg.motor_cmd[waist_roll_idx].kd = self.config.locomotion_arm_waist_kds[1] + self.msg.motor_cmd[waist_roll_idx].tau = 0 + + # Zero out the problematic joints (waist yaw, waist pitch, wrist pitch/yaw) + problematic_joints = [12, 14, 20, 21, 27, 28] + for joint_idx in problematic_joints: + self.msg.motor_cmd[joint_idx].q = 0.0 + self.msg.motor_cmd[joint_idx].qd = 0 + if joint_idx in [12, 14]: # waist + kp_idx = 0 if joint_idx == 12 else 2 # yaw or pitch + self.msg.motor_cmd[joint_idx].kp = self.config.locomotion_arm_waist_kps[kp_idx] + self.msg.motor_cmd[joint_idx].kd = self.config.locomotion_arm_waist_kds[kp_idx] + else: # wrists (20, 21, 27, 28) + self.msg.motor_cmd[joint_idx].kp = self.kp_wrist + self.msg.motor_cmd[joint_idx].kd = self.kd_wrist + self.msg.motor_cmd[joint_idx].tau = 0 + + + # Send command + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + def _locomotion_thread_loop(self): + """Background thread that runs the locomotion policy at specified rate.""" + logger_mp.info("Locomotion thread started") + while self.locomotion_running: + start_time = time.time() + try: + # Use different run function based on policy type + if self.policy_type == 'onnx': + self.groot_locomotion_run() + else: + self.locomotion_run() + except Exception as e: + logger_mp.error(f"Error in locomotion loop: {e}") + + # Sleep to maintain control rate + elapsed = time.time() - start_time + sleep_time = max(0, self.config.locomotion_control_dt - elapsed) + time.sleep(sleep_time) + logger_mp.info("Locomotion thread stopped") + + def start_locomotion_thread(self): + """Start the background locomotion control thread.""" + if not self.config.locomotion_control: + logger_mp.warning("locomotion_control is False, cannot start thread") + return + + if self.locomotion_running: + logger_mp.warning("Locomotion thread already running") + return + + logger_mp.info("Starting locomotion control thread...") + self.locomotion_running = True + self.locomotion_thread = threading.Thread(target=self._locomotion_thread_loop, daemon=True) + self.locomotion_thread.start() + logger_mp.info("Locomotion control thread started!") + + def stop_locomotion_thread(self): + """Stop the background locomotion control thread.""" + if not self.locomotion_running: + return + + logger_mp.info("Stopping locomotion control thread...") + self.locomotion_running = False + if self.locomotion_thread: + self.locomotion_thread.join(timeout=2.0) + logger_mp.info("Locomotion control thread stopped") + + # Also stop keyboard thread if running + if self.keyboard_running: + self.stop_keyboard_controls() + + def _keyboard_listener_thread(self): + """Background thread that listens for keyboard input (sim mode only).""" + print("\n" + "="*60) + print("KEYBOARD CONTROLS ACTIVE!") + print(" W/S: Forward/Backward") + print(" A/D: Left/Right") + print(" Q/E: Rotate Left/Right") + print(" R/F: Raise/Lower Height (±5cm)") + print(" Z: Stop (zero velocity commands)") + print("="*60 + "\n") + + # Save terminal settings + old_settings = None + try: + old_settings = termios.tcgetattr(sys.stdin) + tty.setcbreak(sys.stdin.fileno()) + + while self.keyboard_running: + if select.select([sys.stdin], [], [], 0.1)[0]: + key = sys.stdin.read(1).lower() + + # Velocity commands + if key == 'w': + self.locomotion_cmd[0] += 0.4 # Forward + elif key == 's': + self.locomotion_cmd[0] -= 0.4 # Backward + elif key == 'a': + self.locomotion_cmd[1] += 0.25 # Left + elif key == 'd': + self.locomotion_cmd[1] -= 0.25 # Right + elif key == 'q': + self.locomotion_cmd[2] += 0.5 # Rotate left + elif key == 'e': + self.locomotion_cmd[2] -= 0.5 # Rotate right + elif key == 'z': + self.locomotion_cmd[:] = 0.0 # Stop + + # Height commands (only for GR00T ONNX policies) + elif key == 'r': + self.groot_height_cmd += 0.05 # Raise 5cm + elif key == 'f': + self.groot_height_cmd -= 0.05 # Lower 5cm + + # Clamp commands to reasonable limits + self.locomotion_cmd[0] = np.clip(self.locomotion_cmd[0], -0.8, 0.8) # vx + self.locomotion_cmd[1] = np.clip(self.locomotion_cmd[1], -0.5, 0.5) # vy + self.locomotion_cmd[2] = np.clip(self.locomotion_cmd[2], -1.0, 1.0) # yaw_rate + + # Clamp height (reasonable range: 0.5m to 1.0m) + if hasattr(self, 'groot_height_cmd'): + self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00) + + # Print current commands + print(f"[VEL CMD] vx={self.locomotion_cmd[0]:.2f}, vy={self.locomotion_cmd[1]:.2f}, yaw={self.locomotion_cmd[2]:.2f}", end="") + if hasattr(self, 'groot_height_cmd'): + print(f" | [HEIGHT] {self.groot_height_cmd:.3f}m", end="") + print() # Newline + + finally: + # Restore terminal settings + if old_settings is not None: + termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings) + print("\nKeyboard controls stopped") + + def start_keyboard_controls(self): + """Start the keyboard control thread (sim mode only).""" + if not self.simulation_mode: + logger_mp.warning("Keyboard controls only available in simulation mode") + return + + if self.keyboard_running: + logger_mp.warning("Keyboard controls already running") + return + + self.keyboard_running = True + self.keyboard_thread = threading.Thread(target=self._keyboard_listener_thread, daemon=True) + self.keyboard_thread.start() + logger_mp.info("Keyboard controls started!") + + def stop_keyboard_controls(self): + """Stop the keyboard control thread.""" + if not self.keyboard_running: + return + + logger_mp.info("Stopping keyboard controls...") + self.keyboard_running = False + if self.keyboard_thread: + self.keyboard_thread.join(timeout=2.0) + logger_mp.info("Keyboard controls stopped") + + + def init_locomotion(self): + """Test locomotion control sequence: home arms -> move legs to default -> start policy thread.""" + if not self.config.locomotion_control: + logger_mp.warning("locomotion_control is False, cannot run test sequence") + return + + logger_mp.info("Starting locomotion test sequence...") + + # 1. Home the arms first + logger_mp.info("Homing arms to zero position...") + #self.ctrl_dual_arm_go_home() + + # 2. Move legs to default position + self.locomotion_move_to_default_pos() + + # 3. Wait 3 seconds + time.sleep(3.0) + + # 4. Hold default leg position for 2 seconds + self.locomotion_default_pos_state() + + # 5. Start locomotion policy thread (runs in background) + logger_mp.info("Starting locomotion policy control...") + self.start_locomotion_thread() + + logger_mp.info("Locomotion test sequence complete! Policy is now running in background.") + logger_mp.info("Use robot.stop_locomotion_thread() to stop the policy.") + + def init_groot_locomotion(self): + """Initialize GR00T-style locomotion for ONNX policies (29 DOF, 15D actions).""" + if not self.config.locomotion_control: + logger_mp.warning("locomotion_control is False, cannot run GR00T init") + return + + logger_mp.info("Starting GR00T locomotion initialization...") + + # Move legs to default position (same as regular locomotion) + self.locomotion_move_to_default_pos() + + # Wait 3 seconds + time.sleep(3.0) + + # Hold default leg position for 2 seconds + self.locomotion_default_pos_state() + + # Start locomotion policy thread (will use groot_locomotion_run) + logger_mp.info("Starting GR00T locomotion policy control...") + self.start_locomotion_thread() + + logger_mp.info("GR00T locomotion initialization complete! Policy is now running.") + logger_mp.info("516D observations (86D × 6 frames), 15D actions (legs + waist)") + + def motion_imitation_run(self): + """Motion imitation policy loop - tracks reference motion (dance_102, etc).""" + self.motion_counter += 1 + self.motion_elapsed_time = self.motion_counter * self.config.motion_control_dt + + # Update motion loader to current time + self.motion_loader.update(self.motion_elapsed_time) + + # Get current lowstate + lowstate = self.lowstate_buffer.GetData() + if lowstate is None: + return + + # Get ALL 29 joint positions and velocities from robot + # IMPORTANT: Convert from motor order to BFS order to match reference motion + # The C++ code does: robot_bfs[i] = motor[joint_ids_map[i]] + for i in range(29): + motor_idx = self.config.motion_joint_ids_map[i] + self.motion_qj_all[i] = lowstate.motor_state[motor_idx].q + self.motion_dqj_all[i] = lowstate.motor_state[motor_idx].dq + + # ======== 23 DOF MODE CONFIGURATION ======== + # For real robot - zeros out joints not present in 23 DOF hardware + # Waist: yaw(12), pitch(14) | Wrist: L_pitch/yaw(20,21), R_pitch/yaw(27,28) + USE_23DOF = True # Set to True for real robot without these joints + JOINTS_TO_ZERO_23DOF = [12,14,20, 21, 27, 28]#12, 14, 20, 21, 27, 28]# + + # Apply 23 DOF zeroing to robot observations if enabled + if USE_23DOF: + for joint_idx in JOINTS_TO_ZERO_23DOF: + self.motion_qj_all[joint_idx] = 0.0 + self.motion_dqj_all[joint_idx] = 0.0 + if self.motion_counter == 1: + logger_mp.info("="*60) + logger_mp.info("🤖 23 DOF MODE ENABLED") + logger_mp.info(f" Zeroing joints: {JOINTS_TO_ZERO_23DOF}") + logger_mp.info(" Waist: yaw(12), pitch(14)") + logger_mp.info(" Wrist L: pitch(20), yaw(21) | Wrist R: pitch(27), yaw(28)") + logger_mp.info(" Applied to: robot obs, reference motion, policy actions") + logger_mp.info("="*60) + + # Get IMU data + robot_quat = lowstate.imu_state.quaternion # [w, x, y, z] + ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32) # 3D + + if self.policy is None: + # DIRECT PLAYBACK MODE (no policy) + motion_joint_pos_dfs = self.motion_loader.get_joint_pos() + + # Zero out missing joints for 23 DOF mode + if USE_23DOF: + # Convert to BFS to zero out, then convert back + motion_joint_pos_bfs_temp = np.zeros(29, dtype=np.float32) + for i in range(29): + motion_joint_pos_bfs_temp[i] = motion_joint_pos_dfs[self.config.motion_joint_ids_map[i]] + for joint_idx in JOINTS_TO_ZERO_23DOF: + motion_joint_pos_bfs_temp[joint_idx] = 0.0 + # Convert back to DFS for sending + for i in range(29): + motion_joint_pos_dfs[self.config.motion_joint_ids_map[i]] = motion_joint_pos_bfs_temp[i] + + for i in range(29): + motor_idx = self.config.motion_joint_ids_map[i] + csv_idx = self.config.motion_joint_ids_map[i] + self.msg.motor_cmd[motor_idx].q = motion_joint_pos_dfs[csv_idx] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.motion_stiffness[motor_idx] + self.msg.motor_cmd[motor_idx].kd = self.config.motion_damping[motor_idx] + self.msg.motor_cmd[motor_idx].tau = 0 + else: + # POLICY MODE - Full observation construction and inference + + # ======== DEBUG TEST MODES ======== + # Mode 1: Direct playback (no policy) - set motion_policy_path = None in config instead + # Mode 2: Send default pos (stand still) - TEST_SEND_DEFAULT_POS = True + # Mode 3: Policy with zero reference - TEST_WITH_ZEROS = True, TEST_SEND_DEFAULT_POS = False + # Mode 4: Policy with real reference - TEST_WITH_ZEROS = False, TEST_SEND_DEFAULT_POS = False + TEST_WITH_ZEROS = False # True = use zero reference motion in observation + TEST_SEND_DEFAULT_POS = False # True = bypass policy and send default pos (stand still) + TEST_DIRECT_PLAYBACK = False # True = bypass policy and send reference motion directly + + if TEST_DIRECT_PLAYBACK: + # DEBUG: Play back reference motion without policy + motion_joint_pos_dfs = self.motion_loader.get_joint_pos() # 29D in DFS order + + # Zero out missing joints for 23 DOF mode + if USE_23DOF: + # Convert to BFS to zero out, then convert back + motion_joint_pos_bfs_temp = np.zeros(29, dtype=np.float32) + for i in range(29): + motion_joint_pos_bfs_temp[i] = motion_joint_pos_dfs[self.config.motion_joint_ids_map[i]] + for joint_idx in JOINTS_TO_ZERO_23DOF: + motion_joint_pos_bfs_temp[joint_idx] = 0.0 + # Convert back to DFS for sending + for i in range(29): + motion_joint_pos_dfs[self.config.motion_joint_ids_map[i]] = motion_joint_pos_bfs_temp[i] + + # Send directly to motors using joint_ids_map (same as direct playback mode) + for i in range(29): + motor_idx = self.config.motion_joint_ids_map[i] + csv_idx = self.config.motion_joint_ids_map[i] + self.msg.motor_cmd[motor_idx].q = motion_joint_pos_dfs[csv_idx] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.motion_stiffness[motor_idx] + self.msg.motor_cmd[motor_idx].kd = self.config.motion_damping[motor_idx] + self.msg.motor_cmd[motor_idx].tau = 0 + + if self.motion_counter == 1: + logger_mp.info("="*60) + logger_mp.info("⚠️ DEBUG MODE: DIRECT PLAYBACK (reference motion, no policy)") + logger_mp.info("="*60) + + target_joint_pos_bfs = None # Not used in this mode + + else: + # Run observation construction and policy + if TEST_WITH_ZEROS: + # Send zeros for reference motion + motion_joint_pos_bfs = np.zeros(29, dtype=np.float32) + motion_joint_vel_bfs = np.zeros(29, dtype=np.float32) + if self.motion_counter == 1: + logger_mp.info("="*60) + logger_mp.info("⚠️ DEBUG MODE: Using ZERO reference motion + RUNNING POLICY") + logger_mp.info("="*60) + else: + # Get reference motion (DFS order from CSV) + motion_joint_pos_dfs = self.motion_loader.get_joint_pos() # 29D + motion_joint_vel_dfs = self.motion_loader.get_joint_vel() # 29D + + # Convert from DFS to BFS order: bfs[i] = dfs[joint_ids_map[i]] + motion_joint_pos_bfs = np.zeros(29, dtype=np.float32) + motion_joint_vel_bfs = np.zeros(29, dtype=np.float32) + for i in range(29): + motion_joint_pos_bfs[i] = motion_joint_pos_dfs[self.config.motion_joint_ids_map[i]] + motion_joint_vel_bfs[i] = motion_joint_vel_dfs[self.config.motion_joint_ids_map[i]] + + # Zero out missing joints in reference motion for 23 DOF mode + if USE_23DOF: + for joint_idx in JOINTS_TO_ZERO_23DOF: + motion_joint_pos_bfs[joint_idx] = 0.0 + motion_joint_vel_bfs[joint_idx] = 0.0 + + # Compute motion_anchor_ori_b (6D rotation matrix representation) + motion_quat_wxyz = self.motion_loader.get_root_quat_wxyz() + robot_rot = R.from_quat([robot_quat[1], robot_quat[2], robot_quat[3], robot_quat[0]]).as_matrix() + motion_rot = R.from_quat([motion_quat_wxyz[1], motion_quat_wxyz[2], motion_quat_wxyz[3], motion_quat_wxyz[0]]).as_matrix() + relative_rot = robot_rot.T @ motion_rot + motion_anchor_ori_b = np.array([relative_rot[0, 0], relative_rot[0, 1], + relative_rot[1, 0], relative_rot[1, 1], + relative_rot[2, 0], relative_rot[2, 1]], dtype=np.float32) + + # Compute joint positions and velocities relative to default + default_joint_pos = np.array(self.config.motion_default_joint_pos, dtype=np.float32) + joint_pos_rel = self.motion_qj_all - default_joint_pos + joint_vel_rel = self.motion_dqj_all.copy() + + # Build 154D observation: + # motion_command (58D) = joint_pos (29D) + joint_vel (29D) from reference + # motion_anchor_ori_b (6D) + # base_ang_vel (3D) + # joint_pos_rel (29D) + # joint_vel_rel (29D) + # last_action (29D) + self.motion_obs[0:29] = motion_joint_pos_bfs + self.motion_obs[29:58] = motion_joint_vel_bfs + self.motion_obs[58:64] = motion_anchor_ori_b + self.motion_obs[64:67] = ang_vel + self.motion_obs[67:96] = joint_pos_rel + self.motion_obs[96:125] = joint_vel_rel + self.motion_obs[125:154] = self.motion_action + + if TEST_SEND_DEFAULT_POS: + # DEBUG: Just send default positions (should make robot stand still) + target_joint_pos_bfs = default_joint_pos.copy() + if self.motion_counter == 1: + logger_mp.info("="*60) + logger_mp.info("⚠️ DEBUG MODE: Sending DEFAULT positions (NO POLICY)") + logger_mp.info("="*60) + logger_mp.info(f" Default pos BFS[0:5]: {target_joint_pos_bfs[0:5]}") + if self.motion_counter % 50 == 0: + logger_mp.info(f" [DEFAULT MODE] Sending: [{target_joint_pos_bfs[0]:.4f}, {target_joint_pos_bfs[6]:.4f}, {target_joint_pos_bfs[12]:.4f}]") + logger_mp.info(f" [DEFAULT MODE] Robot at: [{self.motion_qj_all[0]:.4f}, {self.motion_qj_all[6]:.4f}, {self.motion_qj_all[12]:.4f}]") + else: + # Run ONNX policy inference + obs_tensor = torch.from_numpy(self.motion_obs).unsqueeze(0) + ort_inputs = {self.policy.get_inputs()[0].name: obs_tensor.cpu().numpy()} + ort_outs = self.policy.run(None, ort_inputs) + self.motion_action = ort_outs[0].squeeze() # 29D action in BFS order + + # Zero out missing joints in policy actions for 23 DOF mode + if USE_23DOF: + for joint_idx in JOINTS_TO_ZERO_23DOF: + self.motion_action[joint_idx] = 0.0 + + # Process actions: scale and add offset + action_scale = np.array(self.config.motion_action_scale, dtype=np.float32) + target_joint_pos_bfs = default_joint_pos + self.motion_action * action_scale + + # Send commands to motors: motor[joint_ids_map[i]] = action[i] + for i in range(29): + motor_idx = self.config.motion_joint_ids_map[i] + self.msg.motor_cmd[motor_idx].q = target_joint_pos_bfs[i] + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.motion_stiffness[motor_idx] + self.msg.motor_cmd[motor_idx].kd = self.config.motion_damping[motor_idx] + self.msg.motor_cmd[motor_idx].tau = 0 + + # Debug print (only when running policy, not in TEST_SEND_DEFAULT_POS or TEST_DIRECT_PLAYBACK mode) + if self.motion_counter == 1 and self.policy and not TEST_SEND_DEFAULT_POS and not TEST_DIRECT_PLAYBACK: + logger_mp.info("="*60) + logger_mp.info("POLICY MODE OBSERVATION CHECK (First iteration)") + logger_mp.info("="*60) + logger_mp.info(f"Reference motion (BFS) samples: [{motion_joint_pos_bfs[0]:.3f}, {motion_joint_pos_bfs[6]:.3f}, {motion_joint_pos_bfs[12]:.3f}]") + logger_mp.info(f"Robot joints (BFS) samples: [{self.motion_qj_all[0]:.3f}, {self.motion_qj_all[6]:.3f}, {self.motion_qj_all[12]:.3f}]") + logger_mp.info(f"Default positions samples: [{default_joint_pos[0]:.3f}, {default_joint_pos[6]:.3f}, {default_joint_pos[12]:.3f}]") + logger_mp.info(f"Joint pos rel samples: [{joint_pos_rel[0]:.3f}, {joint_pos_rel[6]:.3f}, {joint_pos_rel[12]:.3f}]") + logger_mp.info(f"Joint vel rel samples: [{joint_vel_rel[0]:.3f}, {joint_vel_rel[6]:.3f}, {joint_vel_rel[12]:.3f}]") + logger_mp.info(f"Angular velocity: [{ang_vel[0]:.3f}, {ang_vel[1]:.3f}, {ang_vel[2]:.3f}]") + logger_mp.info(f"Motion anchor ori: [{motion_anchor_ori_b[0]:.3f}, ..., {motion_anchor_ori_b[5]:.3f}]") + logger_mp.info(f"Observation breakdown:") + logger_mp.info(f" [0:29] motion_cmd_pos: range [{self.motion_obs[0:29].min():.3f}, {self.motion_obs[0:29].max():.3f}]") + logger_mp.info(f" [29:58] motion_cmd_vel: range [{self.motion_obs[29:58].min():.3f}, {self.motion_obs[29:58].max():.3f}]") + logger_mp.info(f" [58:64] anchor_ori: range [{self.motion_obs[58:64].min():.3f}, {self.motion_obs[58:64].max():.3f}]") + logger_mp.info(f" [64:67] ang_vel: range [{self.motion_obs[64:67].min():.3f}, {self.motion_obs[64:67].max():.3f}]") + logger_mp.info(f" [67:96] joint_pos_rel: range [{self.motion_obs[67:96].min():.3f}, {self.motion_obs[67:96].max():.3f}]") + logger_mp.info(f" [96:125] joint_vel_rel: range [{self.motion_obs[96:125].min():.3f}, {self.motion_obs[96:125].max():.3f}]") + logger_mp.info(f" [125:154] last_action: range [{self.motion_obs[125:154].min():.3f}, {self.motion_obs[125:154].max():.3f}]") + logger_mp.info(f"Full obs range: [{self.motion_obs.min():.3f}, {self.motion_obs.max():.3f}]") + logger_mp.info(f"Action output (first): [{self.motion_action.min():.3f}, {self.motion_action.max():.3f}]") + logger_mp.info(f"Action scale samples: [{action_scale[0]:.3f}, {action_scale[6]:.3f}, {action_scale[12]:.3f}]") + logger_mp.info(f"Target positions samples: [{target_joint_pos_bfs[0]:.3f}, {target_joint_pos_bfs[6]:.3f}, {target_joint_pos_bfs[12]:.3f}]") + logger_mp.info("="*60) + + if self.motion_counter % 50 == 0: + if self.policy is None: + mode = "DIRECT" + elif TEST_DIRECT_PLAYBACK: + mode = "DIRECT_DEBUG" + elif TEST_SEND_DEFAULT_POS: + mode = "DEFAULT_POS" + elif TEST_WITH_ZEROS: + mode = "POLICY_ZEROS" + else: + mode = "POLICY" + logger_mp.info(f"Motion {mode}: t={self.motion_elapsed_time:.2f}s, frame={self.motion_loader.index_0}/{self.motion_loader.num_frames}") + if self.policy and not TEST_SEND_DEFAULT_POS and not TEST_DIRECT_PLAYBACK: + logger_mp.info(f" Policy action range: [{self.motion_action.min():.3f}, {self.motion_action.max():.3f}]") + logger_mp.info(f" Sample actions[0,6,12]: [{self.motion_action[0]:.3f}, {self.motion_action[6]:.3f}, {self.motion_action[12]:.3f}]") + logger_mp.info(f" Target pos (after scale)[0,6,12]: [{target_joint_pos_bfs[0]:.3f}, {target_joint_pos_bfs[6]:.3f}, {target_joint_pos_bfs[12]:.3f}]") + logger_mp.info(f" Robot pos (BFS)[0,6,12]: [{self.motion_qj_all[0]:.3f}, {self.motion_qj_all[6]:.3f}, {self.motion_qj_all[12]:.3f}]") + + # Send command + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + + def _motion_imitation_thread_loop(self): + """Background thread that runs the motion imitation policy at specified rate.""" + logger_mp.info("Motion imitation thread started") + while self.motion_imitation_running: + start_time = time.time() + try: + self.motion_imitation_run() + except Exception as e: + logger_mp.error(f"Error in motion imitation loop: {e}") + import traceback + traceback.print_exc() + + # Sleep to maintain control rate + elapsed = time.time() - start_time + sleep_time = max(0, self.config.motion_control_dt - elapsed) + time.sleep(sleep_time) + logger_mp.info("Motion imitation thread stopped") + + def start_motion_imitation_thread(self): + """Start the background motion imitation control thread.""" + if not self.config.motion_imitation_control: + logger_mp.warning("motion_imitation_control is False, cannot start thread") + return + + if self.motion_imitation_running: + logger_mp.warning("Motion imitation thread already running") + return + + logger_mp.info("Starting motion imitation control thread...") + self.motion_imitation_running = True + self.motion_imitation_thread = threading.Thread(target=self._motion_imitation_thread_loop, daemon=True) + self.motion_imitation_thread.start() + logger_mp.info("Motion imitation control thread started!") + + def stop_motion_imitation_thread(self): + """Stop the background motion imitation control thread.""" + if not self.motion_imitation_running: + return + + logger_mp.info("Stopping motion imitation control thread...") + self.motion_imitation_running = False + if self.motion_imitation_thread: + self.motion_imitation_thread.join(timeout=2.0) + logger_mp.info("Motion imitation control thread stopped") + + def init_motion_imitation(self): + """Initialize motion imitation - move to default standing pose and start policy.""" + if not self.config.motion_imitation_control: + logger_mp.warning("motion_imitation_control is False, cannot run initialization") + return + + logger_mp.info("Starting motion imitation initialization...") + + # Move to default standing position + logger_mp.info("Moving to default standing position...") + total_time = 3.0 + num_steps = int(total_time / self.config.motion_control_dt) + + # Get current positions (in motor order) + current_q_motor = self.get_current_motor_q() + + # target_q is in BFS order from config, need to convert to motor order + target_q_bfs = np.array(self.config.motion_default_joint_pos, dtype=np.float32) + target_q_motor = np.zeros(29, dtype=np.float32) + for i in range(29): + motor_idx = self.config.motion_joint_ids_map[i] + target_q_motor[motor_idx] = target_q_bfs[i] + + # Interpolate to target (both in motor order now) + for i in range(num_steps): + alpha = i / num_steps + for motor_idx in range(29): + self.msg.motor_cmd[motor_idx].q = current_q_motor[motor_idx] * (1 - alpha) + target_q_motor[motor_idx] * alpha + self.msg.motor_cmd[motor_idx].qd = 0 + self.msg.motor_cmd[motor_idx].kp = self.config.motion_stiffness[motor_idx] + self.msg.motor_cmd[motor_idx].kd = self.config.motion_damping[motor_idx] + self.msg.motor_cmd[motor_idx].tau = 0 + self.msg.crc = self.crc.Crc(self.msg) + self.lowcmd_publisher.Write(self.msg) + time.sleep(self.config.motion_control_dt) + + logger_mp.info("Reached default position") + + # Wait 2 seconds + time.sleep(2.0) + + # Start motion imitation policy thread + logger_mp.info("Starting motion imitation policy control...") + self.start_motion_imitation_thread() + + logger_mp.info("Motion imitation initialization complete! Policy is now running.") + logger_mp.info(f"154D observations, 29D actions. Motion duration: {self.motion_loader.duration:.2f}s") class G1_29_JointArmIndex(IntEnum): @@ -705,7 +1890,6 @@ class G1_29_JointArmIndex(IntEnum): kRightWristPitch = 27 kRightWristYaw = 28 - class G1_29_JointIndex(IntEnum): # Left leg kLeftHipPitch = 0 @@ -751,5 +1935,4 @@ class G1_29_JointIndex(IntEnum): kNotUsedJoint2 = 31 kNotUsedJoint3 = 32 kNotUsedJoint4 = 33 - kNotUsedJoint5 = 34 - + kNotUsedJoint5 = 34 \ No newline at end of file diff --git a/test_cool_config.json b/test_cool_config.json new file mode 100644 index 000000000..1ee91de98 --- /dev/null +++ b/test_cool_config.json @@ -0,0 +1,71 @@ +{ + "glove": { + "base_class": "lerobot.teleoperators.homunculus.homunculus_glove.HomunculusGlove", + "port": "/dev/ttyACM0", + "id": "unitree_glove_test", + "side": "right", + "baud_rate": 115200, + "robot_actions": { + "kRightShoulderPitch.pos": { + "source": "teleop", + "joint": "index_mcp_flexion" + }, + "kRightShoulderRoll.pos": { + "source": "neutral", + "value": 0.5 + }, + "kRightShoulderYaw.pos": { + "source": "teleop", + "joint": "thumb_cmc" + }, + "kRightElbow.pos": { + "source": "teleop", + "joint": "middle_mcp_flexion", + "invert": true + }, + "kRightWristRoll.pos": { + "source": "teleop", + "joint": "middle_dip" + }, + "kRightWristPitch.pos": { + "source": "neutral", + "value": 0.5 + }, + "kRightWristYaw.pos": { + "source": "neutral", + "value": 0.5 + }, + "kLeftShoulderPitch.pos": { + "source": "teleop", + "joint": "pinky_mcp_flexion" + }, + "kLeftShoulderRoll.pos": { + "source": "neutral", + "value": 0.5 + }, + "kLeftShoulderYaw.pos": { + "source": "teleop", + "joint": "thumb_cmc", + "invert": true + }, + "kLeftElbow.pos": { + "source": "teleop", + "joint": "ring_mcp_flexion", + "invert": true + }, + "kLeftWristRoll.pos": { + "source": "teleop", + "joint": "ring_dip" + }, + "kLeftWristPitch.pos": { + "source": "neutral", + "value": 0.5 + }, + "kLeftWristyaw.pos": { + "source": "neutral", + "value": 0.5 + } + } + } +} + diff --git a/unitree_sim_isaaclab b/unitree_sim_isaaclab new file mode 160000 index 000000000..5b89b377b --- /dev/null +++ b/unitree_sim_isaaclab @@ -0,0 +1 @@ +Subproject commit 5b89b377bcf576eb256aaa994ada2b01c97cdfa1 diff --git a/utils/constants.py b/utils/constants.py new file mode 100644 index 000000000..5d14f3886 --- /dev/null +++ b/utils/constants.py @@ -0,0 +1,280 @@ +import dataclasses +from typing import List, Dict + + +@dataclasses.dataclass(frozen=True) +class RobotConfig: + motors: List[str] + cameras: List[str] + camera_to_image_key: Dict[str, str] + json_state_data_name: List[str] + json_action_data_name: List[str] + + +Z1_CONFIG = RobotConfig( + motors=[ + "kLeftWaist", + "kLeftShoulder", + "kLeftElbow", + "kLeftForearmRoll", + "kLeftWristAngle", + "kLeftWristRotate", + "kLeftGripper", + "kRightWaist", + "kRightShoulder", + "kRightElbow", + "kRightForearmRoll", + "kRightWristAngle", + "kRightWristRotate", + "kRightGripper", + ], + cameras=[ + "cam_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={"color_0": "cam_high", "color_1": "cam_left_wrist", "color_2": "cam_right_wrist"}, + json_state_data_name=["left_arm", "right_arm"], + json_action_data_name=["left_arm", "right_arm"], +) + + +Z1_SINGLE_CONFIG = RobotConfig( + motors=[ + "kWaist", + "kShoulder", + "kElbow", + "kForearmRoll", + "kWristAngle", + "kWristRotate", + "kGripper", + ], + cameras=[ + "cam_high", + "cam_wrist", + ], + camera_to_image_key={"color_0": "cam_high", "color_1": "cam_wrist"}, + json_state_data_name=["left_arm", "right_arm"], + json_action_data_name=["left_arm", "right_arm"], +) + + +G1_DEX1_CONFIG = RobotConfig( + motors=[ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristYaw", + "kRightShoulderPitch", + "kRightShoulderRoll", + "kRightShoulderYaw", + "kRightElbow", + "kRightWristRoll", + "kRightWristPitch", + "kRightWristYaw", + "kLeftGripper", + "kRightGripper", + ], + cameras=[ + "cam_left_high", + "cam_right_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={ + "color_0": "cam_left_high", + "color_1": "cam_right_high", + "color_2": "cam_left_wrist", + "color_3": "cam_right_wrist", + }, + json_state_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], + json_action_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], +) + + +G1_DEX1_CONFIG_SIM = RobotConfig( + motors=[ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristYaw", + "kRightShoulderPitch", + "kRightShoulderRoll", + "kRightShoulderYaw", + "kRightElbow", + "kRightWristRoll", + "kRightWristPitch", + "kRightWristYaw", + "kLeftGripper", + "kRightGripper", + ], + cameras=[ + "cam_left_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={ + "color_0": "cam_left_high", + "color_1": "cam_left_wrist", + "color_2": "cam_right_wrist", + }, + json_state_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], + json_action_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], +) + + +G1_DEX3_CONFIG = RobotConfig( + motors=[ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristYaw", + "kRightShoulderPitch", + "kRightShoulderRoll", + "kRightShoulderYaw", + "kRightElbow", + "kRightWristRoll", + "kRightWristPitch", + "kRightWristYaw", + "kLeftHandThumb0", + "kLeftHandThumb1", + "kLeftHandThumb2", + "kLeftHandMiddle0", + "kLeftHandMiddle1", + "kLeftHandIndex0", + "kLeftHandIndex1", + "kRightHandThumb0", + "kRightHandThumb1", + "kRightHandThumb2", + "kRightHandIndex0", + "kRightHandIndex1", + "kRightHandMiddle0", + "kRightHandMiddle1", + ], + cameras=[ + "cam_left_high", + "cam_right_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={ + "color_0": "cam_left_high", + "color_1": "cam_right_high", + "color_2": "cam_left_wrist", + "color_3": "cam_right_wrist", + }, + json_state_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], + json_action_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], +) + + +G1_BRAINCO_CONFIG = RobotConfig( + motors=[ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristYaw", + "kRightShoulderPitch", + "kRightShoulderRoll", + "kRightShoulderYaw", + "kRightElbow", + "kRightWristRoll", + "kRightWristPitch", + "kRightWristYaw", + "kLeftHandThumb", + "kLeftHandThumbAux", + "kLeftHandIndex", + "kLeftHandMiddle", + "kLeftHandRing", + "kLeftHandPinky", + "kRightHandThumb", + "kRightHandThumbAux", + "kRightHandIndex", + "kRightHandMiddle", + "kRightHandRing", + "kRightHandPinky", + ], + cameras=[ + "cam_left_high", + "cam_right_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={ + "color_0": "cam_left_high", + "color_1": "cam_right_high", + "color_2": "cam_left_wrist", + "color_3": "cam_right_wrist", + }, + json_state_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], + json_action_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], +) + + +G1_INSPIRE_CONFIG = RobotConfig( + motors=[ + "kLeftShoulderPitch", + "kLeftShoulderRoll", + "kLeftShoulderYaw", + "kLeftElbow", + "kLeftWristRoll", + "kLeftWristPitch", + "kLeftWristYaw", + "kRightShoulderPitch", + "kRightShoulderRoll", + "kRightShoulderYaw", + "kRightElbow", + "kRightWristRoll", + "kRightWristPitch", + "kRightWristYaw", + "kLeftHandPinky", + "kLeftHandRing", + "kLeftHandMiddle", + "kLeftHandIndex", + "kLeftHandThumbBend", + "kLeftHandThumbRotation", + "kRightHandPinky", + "kRightHandRing", + "kRightHandMiddle", + "kRightHandIndex", + "kRightHandThumbBend", + "kRightHandThumbRotation", + ], + cameras=[ + "cam_left_high", + "cam_right_high", + "cam_left_wrist", + "cam_right_wrist", + ], + camera_to_image_key={ + "color_0": "cam_left_high", + "color_1": "cam_right_high", + "color_2": "cam_left_wrist", + "color_3": "cam_right_wrist", + }, + json_state_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], + json_action_data_name=["left_arm", "right_arm", "left_ee", "right_ee"], +) + + +ROBOT_CONFIGS = { + "Unitree_Z1_Single": Z1_SINGLE_CONFIG, + "Unitree_Z1_Dual": Z1_CONFIG, + "Unitree_G1_Dex1": G1_DEX1_CONFIG, + "Unitree_G1_Dex1_Sim": G1_DEX1_CONFIG_SIM, + "Unitree_G1_Dex3": G1_DEX3_CONFIG, + "Unitree_G1_Brainco": G1_BRAINCO_CONFIG, + "Unitree_G1_Inspire": G1_INSPIRE_CONFIG, +} diff --git a/utils/convert_lerobot_to_h5.py b/utils/convert_lerobot_to_h5.py new file mode 100644 index 000000000..ac1b0df4c --- /dev/null +++ b/utils/convert_lerobot_to_h5.py @@ -0,0 +1,161 @@ +""" +Script lerobot to h5. +# --repo-id Your unique repo ID on Hugging Face Hub +# --output_dir Save path to h5 file + +python unitree_lerobot/utils/convert_lerobot_to_h5.py.py \ + --repo-id your_name/g1_grabcube_double_hand \ + --output_dir "$HOME/datasets/g1_grabcube_double_hand" +""" + +import os +import cv2 +import h5py +import tyro +import numpy as np +from tqdm import tqdm +from pathlib import Path +from collections import defaultdict +from lerobot.datasets.lerobot_dataset import LeRobotDataset + + +class LeRobotDataProcessor: + def __init__(self, repo_id: str, root: str = None, image_dtype: str = "to_unit8") -> None: + self.image_dtype = image_dtype + self.dataset = LeRobotDataset(repo_id=repo_id, root=root) + + def process_episode(self, episode_index: int) -> dict: + """Process a single episode to extract camera images, state, and action.""" + from_idx = self.dataset.episode_data_index["from"][episode_index].item() + to_idx = self.dataset.episode_data_index["to"][episode_index].item() + + episode = defaultdict(list) + cameras = defaultdict(list) + + for step_idx in tqdm( + range(from_idx, to_idx), desc=f"Episode {episode_index}", position=1, leave=False, dynamic_ncols=True + ): + step = self.dataset[step_idx] + + image_dict = { + key.split(".")[2]: cv2.cvtColor( + np.transpose((value.numpy() * 255).astype(np.uint8), (1, 2, 0)), cv2.COLOR_BGR2RGB + ) + for key, value in step.items() + if key.startswith("observation.image") and len(key.split(".")) >= 3 + } + + for key, value in image_dict.items(): + if self.image_dtype == "to_unit8": + cameras[key].append(value) + elif self.image_dtype == "to_bytes": + success, encoded_img = cv2.imencode(".jpg", value, [cv2.IMWRITE_JPEG_QUALITY, 100]) + if not success: + raise ValueError(f"Image encoding failed for key: {key}") + cameras[key].append(np.void(encoded_img.tobytes())) + + cam_height, cam_width = next(iter(image_dict.values())).shape[:2] + episode["state"].append(step["observation.state"]) + episode["action"].append(step["action"]) + + episode["cameras"] = cameras + episode["task"] = step["task"] + episode["episode_length"] = to_idx - from_idx + + # Data configuration for later use + episode["data_cfg"] = { + "camera_names": list(image_dict.keys()), + "cam_height": cam_height, + "cam_width": cam_width, + "state_dim": np.squeeze(step["observation.state"].numpy().shape), + "action_dim": np.squeeze(step["action"].numpy().shape), + } + episode["episode_index"] = episode_index + + return episode + + +class H5Writer: + def __init__(self, output_dir: Path) -> None: + self.output_dir = output_dir + os.makedirs(output_dir, exist_ok=True) + + def write_to_h5(self, episode: dict) -> None: + """Write episode data to HDF5 file.""" + + episode_length = episode["episode_length"] + episode_index = episode["episode_index"] + state = episode["state"] + action = episode["action"] + qvel = np.zeros_like(episode["state"]) + cameras = episode["cameras"] + task = episode["task"] + data_cfg = episode["data_cfg"] + + # Prepare data dictionary + data_dict = { + "/observations/qpos": [state], + "/observations/qvel": [qvel], + "/action": [action], + **{f"/observations/images/{k}": [v] for k, v in cameras.items()}, + } + + h5_path = os.path.join(self.output_dir, f"episode_{episode_index}.hdf5") + + with h5py.File(h5_path, "w", rdcc_nbytes=1024**2 * 2, libver="latest") as root: + # Set attributes + root.attrs["sim"] = False + + # Create datasets + obs = root.create_group("observations") + image = obs.create_group("images") + + # Write camera images + for cam_name, images in cameras.items(): + data_dtype = images[0].dtype + shape = ( + (episode_length, data_cfg["cam_height"], data_cfg["cam_width"], 3) + if data_dtype == "uint8" + else (episode_length,) + ) + chunks = (1, data_cfg["cam_height"], data_cfg["cam_width"], 3) if data_dtype == "uint8" else (1,) + image.create_dataset(cam_name, shape=shape, dtype=data_dtype, chunks=chunks, compression="gzip") + # root[f'/observations/images/{cam_name}'][...] = images + + # Write state and action data + obs.create_dataset("qpos", (episode_length, data_cfg["state_dim"]), dtype="float32", compression="gzip") + obs.create_dataset("qvel", (episode_length, data_cfg["state_dim"]), dtype="float32", compression="gzip") + root.create_dataset("action", (episode_length, data_cfg["action_dim"]), dtype="float32", compression="gzip") + + # Write metadata + root.create_dataset("is_edited", (1,), dtype="uint8") + substep_reasonings = root.create_dataset( + "substep_reasonings", (episode_length,), dtype=h5py.string_dtype(encoding="utf-8"), compression="gzip" + ) + root.create_dataset("language_raw", data=task) + substep_reasonings[:] = [task] * episode_length + + # Write additional data + for name, array in data_dict.items(): + root[name][...] = array + + +def lerobot_to_h5(repo_id: str, output_dir: Path, root: str = None) -> None: + """Main function to process and write LeRobot data to HDF5 format.""" + + # Initialize data processor and H5 writer + data_processor = LeRobotDataProcessor( + repo_id, root, image_dtype="to_unit8" + ) # image_dtype Options: "to_unit8", "to_bytes" + h5_writer = H5Writer(output_dir) + + # Process each episode + for episode_index in tqdm( + range(data_processor.dataset.num_episodes), desc="Episodes", position=0, dynamic_ncols=True + ): + episode = data_processor.process_episode(episode_index) + h5_writer.write_to_h5(episode) + + +if __name__ == "__main__": + tyro.cli(lerobot_to_h5) diff --git a/utils/convert_unitree_json_to_h5.py b/utils/convert_unitree_json_to_h5.py new file mode 100644 index 000000000..d848f4dce --- /dev/null +++ b/utils/convert_unitree_json_to_h5.py @@ -0,0 +1,250 @@ +""" +Script Json to h5. + +# --data_dirs Corresponds to the directory of your JSON dataset +# --output_dir Save path to h5 file +# --robot_type The type of the robot used in the dataset (e.g., Unitree_Z1_Single, Unitree_Z1_Dual, Unitree_G1_Dex1, Unitree_G1_Dex3, Unitree_G1_Brainco, Unitree_G1_Inspire) + +python unitree_lerobot/utils/convert_unitree_json_to_h5.py \ + --data_dirs $HOME/datasets/json \ + --output_dir $HOME/datasets/h5 \ + --robot_type Unitree_G1_Dex3 +""" + +import os +import tyro +import json +import h5py +import cv2 +import tqdm +import glob +import numpy as np +from pathlib import Path +from typing import List, Dict, Optional +from collections import defaultdict +from unitree_lerobot.utils.constants import ROBOT_CONFIGS + + +class JsonDataset: + def __init__(self, data_dirs: Path, robot_type: str) -> None: + """ + Initialize the dataset for loading and processing HDF5 files containing robot manipulation data. + + Args: + data_dirs: Path to directory containing training data + """ + assert data_dirs is not None, "Data directory cannot be None" + assert robot_type is not None, "Robot type cannot be None" + self.data_dirs = data_dirs + self.json_file = "data.json" + + # Initialize paths and cache + self._init_paths() + self._init_cache() + self.json_state_data_name = ROBOT_CONFIGS[robot_type].json_state_data_name + self.json_action_data_name = ROBOT_CONFIGS[robot_type].json_action_data_name + self.camera_to_image_key = ROBOT_CONFIGS[robot_type].camera_to_image_key + + def _init_paths(self) -> None: + """Initialize episode and task paths.""" + + self.episode_paths = [] + self.task_paths = [] + + for task_path in glob.glob(os.path.join(self.data_dirs, "*")): + if os.path.isdir(task_path): + episode_paths = glob.glob(os.path.join(task_path, "*")) + if episode_paths: + self.task_paths.append(task_path) + self.episode_paths.extend(episode_paths) + + self.episode_paths = sorted(self.episode_paths) + self.episode_ids = list(range(len(self.episode_paths))) + + def __len__(self) -> int: + """Return the number of episodes in the dataset.""" + return len(self.episode_paths) + + def _init_cache(self) -> List: + """Initialize data cache if enabled.""" + + self.episodes_data_cached = [] + for episode_path in tqdm.tqdm(self.episode_paths, desc="Loading Cache Json"): + json_path = os.path.join(episode_path, self.json_file) + with open(json_path, "r", encoding="utf-8") as jsonf: + self.episodes_data_cached.append(json.load(jsonf)) + + print(f"==> Cached {len(self.episodes_data_cached)} episodes") + + return self.episodes_data_cached + + def _extract_data(self, episode_data: Dict, key: str, parts: List[str]) -> np.ndarray: + """ + Extract data from episode dictionary for specified parts. + + Args: + episode_data: Dictionary containing episode data + key: Data key to extract ('states' or 'actions') + parts: List of parts to include ('left_arm', 'right_arm') + + Returns: + Concatenated numpy array of the requested data + """ + result = [] + for sample_data in episode_data["data"]: + data_array = np.array([], dtype=np.float32) + for part in parts: + if part in sample_data[key] and sample_data[key][part] is not None: + qpos = np.array(sample_data[key][part]["qpos"], dtype=np.float32) + data_array = np.concatenate([data_array, qpos]) + result.append(data_array) + return np.array(result) + + def _parse_images(self, episode_path: str, episode_data) -> dict[str, list[np.ndarray]]: + """Load and stack images for a given camera key.""" + + images = defaultdict(list) + + keys = episode_data["data"][0]["colors"].keys() + cameras = [key for key in keys if "depth" not in key] + + for camera in cameras: + image_key = self.camera_to_image_key.get(camera) + + for sample_data in episode_data["data"]: + image_path = os.path.join(episode_path, sample_data["colors"].get(camera)) + if not os.path.exists(image_path): + raise FileNotFoundError(f"Image path does not exist: {image_path}") + + image = cv2.imread(image_path) + if image is None: + raise RuntimeError(f"Failed to read image: {image_path}") + + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + images[image_key].append(image_rgb) + + return images + + def get_item( + self, + index: Optional[int] = None, + ) -> Dict: + """Get a training sample from the dataset.""" + + file_path = np.random.choice(self.episode_paths) if index is None else self.episode_paths[index] + episode_data = self.episodes_data_cached[index] + + # Load state and action data + action = self._extract_data(episode_data, "actions", self.json_action_data_name) + state = self._extract_data(episode_data, "states", self.json_state_data_name) + episode_length = len(state) + state_dim = state.shape[1] if len(state.shape) == 2 else state.shape[0] + action_dim = action.shape[1] if len(action.shape) == 2 else state.shape[0] + + # Load task description + task = episode_data.get("text", {}).get("goal", "") + + # Load camera images + cameras = self._parse_images(file_path, episode_data) + + # Extract camera configuration + cam_height, cam_width = next(img for imgs in cameras.values() if imgs for img in imgs).shape[:2] + data_cfg = { + "camera_names": list(cameras.keys()), + "cam_height": cam_height, + "cam_width": cam_width, + "state_dim": state_dim, + "action_dim": action_dim, + } + + return { + "episode_index": index, + "episode_length": episode_length, + "state": state, + "action": action, + "cameras": cameras, + "task": task, + "data_cfg": data_cfg, + } + + +class H5Writer: + def __init__(self, output_dir: Path) -> None: + self.output_dir = output_dir + os.makedirs(output_dir, exist_ok=True) + + def write_to_h5(self, episode: dict) -> None: + """Write episode data to HDF5 file.""" + + episode_length = episode["episode_length"] + episode_index = episode["episode_index"] + state = episode["state"] + action = episode["action"] + qvel = np.zeros_like(episode["state"]) + cameras = episode["cameras"] + task = episode["task"] + data_cfg = episode["data_cfg"] + + # Prepare data dictionary + data_dict = { + "/observations/qpos": [state], + "/observations/qvel": [qvel], + "/action": [action], + **{f"/observations/images/{k}": [v] for k, v in cameras.items()}, + } + + h5_path = os.path.join(self.output_dir, f"episode_{episode_index}.hdf5") + + with h5py.File(h5_path, "w", rdcc_nbytes=1024**2 * 2, libver="latest") as root: + # Set attributes + root.attrs["sim"] = False + + # Create datasets + obs = root.create_group("observations") + image = obs.create_group("images") + + # Write camera images + for cam_name, images in cameras.items(): + image.create_dataset( + cam_name, + shape=(episode_length, data_cfg["cam_height"], data_cfg["cam_width"], 3), + dtype="uint8", + chunks=(1, data_cfg["cam_height"], data_cfg["cam_width"], 3), + compression="gzip", + ) + # root[f'/observations/images/{cam_name}'][...] = images + + # Write state and action data + obs.create_dataset("qpos", (episode_length, data_cfg["state_dim"]), dtype="float32", compression="gzip") + obs.create_dataset("qvel", (episode_length, data_cfg["state_dim"]), dtype="float32", compression="gzip") + root.create_dataset("action", (episode_length, data_cfg["action_dim"]), dtype="float32", compression="gzip") + + # Write metadata + root.create_dataset("is_edited", (1,), dtype="uint8") + substep_reasonings = root.create_dataset( + "substep_reasonings", (episode_length,), dtype=h5py.string_dtype(encoding="utf-8"), compression="gzip" + ) + root.create_dataset("language_raw", data=task) + substep_reasonings[:] = [task] * episode_length + + # Write additional data + for name, array in data_dict.items(): + root[name][...] = array + + +def json_to_h5( + data_dirs: Path, + output_dir: Path, + robot_type: str, +) -> None: + """Convert JSON episode data to HDF5 format.""" + dataset = JsonDataset(data_dirs, robot_type) + h5_writer = H5Writer(output_dir) + + for i in tqdm.tqdm(range(len(dataset))): + episode = dataset.get_item(i) + h5_writer.write_to_h5(episode) + + +if __name__ == "__main__": + tyro.cli(json_to_h5) diff --git a/utils/convert_unitree_json_to_lerobot.py b/utils/convert_unitree_json_to_lerobot.py new file mode 100644 index 000000000..3f7ed607c --- /dev/null +++ b/utils/convert_unitree_json_to_lerobot.py @@ -0,0 +1,341 @@ +""" +Script Json to Lerobot. + +# --raw-dir Corresponds to the directory of your JSON dataset +# --repo-id Your unique repo ID on Hugging Face Hub +# --robot_type The type of the robot used in the dataset (e.g., Unitree_Z1_Single, Unitree_Z1_Dual, Unitree_G1_Dex1, Unitree_G1_Dex3, Unitree_G1_Brainco, Unitree_G1_Inspire) +# --push_to_hub Whether or not to upload the dataset to Hugging Face Hub (true or false) + +python unitree_lerobot/utils/convert_unitree_json_to_lerobot.py \ + --raw-dir $HOME/datasets/g1_grabcube_double_hand \ + --repo-id your_name/g1_grabcube_double_hand \ + --robot_type Unitree_G1_Dex3 \ + --push_to_hub +""" + +import os +import cv2 +import tqdm +import tyro +import json +import glob +import dataclasses +import shutil +import numpy as np +from pathlib import Path +from collections import defaultdict +from typing import Literal, List, Dict, Optional + +from lerobot.constants import HF_LEROBOT_HOME +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +from unitree_lerobot.utils.constants import ROBOT_CONFIGS + + +@dataclasses.dataclass(frozen=True) +class DatasetConfig: + use_videos: bool = True + tolerance_s: float = 0.0001 + image_writer_processes: int = 10 + image_writer_threads: int = 5 + video_backend: str | None = None + + +DEFAULT_DATASET_CONFIG = DatasetConfig() + + +class JsonDataset: + def __init__(self, data_dirs: Path, robot_type: str) -> None: + """ + Initialize the dataset for loading and processing HDF5 files containing robot manipulation data. + + Args: + data_dirs: Path to directory containing training data + """ + assert data_dirs is not None, "Data directory cannot be None" + assert robot_type is not None, "Robot type cannot be None" + self.data_dirs = data_dirs + self.json_file = "data.json" + + # Initialize paths and cache + self._init_paths() + self._init_cache() + self.json_state_data_name = ROBOT_CONFIGS[robot_type].json_state_data_name + self.json_action_data_name = ROBOT_CONFIGS[robot_type].json_action_data_name + self.camera_to_image_key = ROBOT_CONFIGS[robot_type].camera_to_image_key + + def _init_paths(self) -> None: + """Initialize episode and task paths.""" + + self.episode_paths = [] + self.task_paths = [] + + for task_path in glob.glob(os.path.join(self.data_dirs, "*")): + if os.path.isdir(task_path): + episode_paths = glob.glob(os.path.join(task_path, "*")) + if episode_paths: + self.task_paths.append(task_path) + self.episode_paths.extend(episode_paths) + + self.episode_paths = sorted(self.episode_paths) + self.episode_ids = list(range(len(self.episode_paths))) + + def __len__(self) -> int: + """Return the number of episodes in the dataset.""" + return len(self.episode_paths) + + def _init_cache(self) -> List: + """Initialize data cache if enabled.""" + + self.episodes_data_cached = [] + for episode_path in tqdm.tqdm(self.episode_paths, desc="Loading Cache Json"): + json_path = os.path.join(episode_path, self.json_file) + with open(json_path, "r", encoding="utf-8") as jsonf: + self.episodes_data_cached.append(json.load(jsonf)) + + print(f"==> Cached {len(self.episodes_data_cached)} episodes") + + return self.episodes_data_cached + + def _extract_data(self, episode_data: Dict, key: str, parts: List[str]) -> np.ndarray: + """ + Extract data from episode dictionary for specified parts. + + Args: + episode_data: Dictionary containing episode data + key: Data key to extract ('states' or 'actions') + parts: List of parts to include ('left_arm', 'right_arm') + + Returns: + Concatenated numpy array of the requested data + """ + result = [] + for sample_data in episode_data["data"]: + data_array = np.array([], dtype=np.float32) + for part in parts: + if part in sample_data[key] and sample_data[key][part] is not None: + qpos = np.array(sample_data[key][part]["qpos"], dtype=np.float32) + data_array = np.concatenate([data_array, qpos]) + result.append(data_array) + return np.array(result) + + def _parse_images(self, episode_path: str, episode_data) -> dict[str, list[np.ndarray]]: + """Load and stack images for a given camera key.""" + + images = defaultdict(list) + + keys = episode_data["data"][0]["colors"].keys() + cameras = [key for key in keys if "depth" not in key] + + for camera in cameras: + image_key = self.camera_to_image_key.get(camera) + if image_key is None: + continue + + for sample_data in episode_data["data"]: + relative_path = sample_data["colors"].get(camera) + if not relative_path: + continue + + image_path = os.path.join(episode_path, relative_path) + if not os.path.exists(image_path): + raise FileNotFoundError(f"Image path does not exist: {image_path}") + + image = cv2.imread(image_path) + if image is None: + raise RuntimeError(f"Failed to read image: {image_path}") + + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + images[image_key].append(image_rgb) + + return images + + def get_item( + self, + index: Optional[int] = None, + ) -> Dict: + """Get a training sample from the dataset.""" + + file_path = np.random.choice(self.episode_paths) if index is None else self.episode_paths[index] + episode_data = self.episodes_data_cached[index] + + # Load state and action data + action = self._extract_data(episode_data, "actions", self.json_action_data_name) + state = self._extract_data(episode_data, "states", self.json_state_data_name) + episode_length = len(state) + state_dim = state.shape[1] if len(state.shape) == 2 else state.shape[0] + action_dim = action.shape[1] if len(action.shape) == 2 else state.shape[0] + + # Load task description + task = episode_data.get("text", {}).get("goal", "") + + # Load camera images + cameras = self._parse_images(file_path, episode_data) + + # Extract camera configuration + cam_height, cam_width = next(img for imgs in cameras.values() if imgs for img in imgs).shape[:2] + data_cfg = { + "camera_names": list(cameras.keys()), + "cam_height": cam_height, + "cam_width": cam_width, + "state_dim": state_dim, + "action_dim": action_dim, + } + + return { + "episode_index": index, + "episode_length": episode_length, + "state": state, + "action": action, + "cameras": cameras, + "task": task, + "data_cfg": data_cfg, + } + + +def create_empty_dataset( + repo_id: str, + robot_type: str, + mode: Literal["video", "image"] = "video", + *, + has_velocity: bool = False, + has_effort: bool = False, + dataset_config: DatasetConfig = DEFAULT_DATASET_CONFIG, +) -> LeRobotDataset: + motors = ROBOT_CONFIGS[robot_type].motors + cameras = ROBOT_CONFIGS[robot_type].cameras + + features = { + "observation.state": { + "dtype": "float32", + "shape": (len(motors),), + "names": [ + motors, + ], + }, + "action": { + "dtype": "float32", + "shape": (len(motors),), + "names": [ + motors, + ], + }, + } + + if has_velocity: + features["observation.velocity"] = { + "dtype": "float32", + "shape": (len(motors),), + "names": [ + motors, + ], + } + + if has_effort: + features["observation.effort"] = { + "dtype": "float32", + "shape": (len(motors),), + "names": [ + motors, + ], + } + + for cam in cameras: + features[f"observation.images.{cam}"] = { + "dtype": mode, + "shape": (480, 640, 3), + "names": [ + "height", + "width", + "channel", + ], + } + + if Path(HF_LEROBOT_HOME / repo_id).exists(): + shutil.rmtree(HF_LEROBOT_HOME / repo_id) + + return LeRobotDataset.create( + repo_id=repo_id, + fps=30, + robot_type=robot_type, + features=features, + use_videos=dataset_config.use_videos, + tolerance_s=dataset_config.tolerance_s, + image_writer_processes=dataset_config.image_writer_processes, + image_writer_threads=dataset_config.image_writer_threads, + video_backend=dataset_config.video_backend, + ) + + +def populate_dataset( + dataset: LeRobotDataset, + raw_dir: Path, + robot_type: str, +) -> LeRobotDataset: + json_dataset = JsonDataset(raw_dir, robot_type) + for i in tqdm.tqdm(range(len(json_dataset))): + episode = json_dataset.get_item(i) + + state = episode["state"] + action = episode["action"] + cameras = episode["cameras"] + task = episode["task"] + episode_length = episode["episode_length"] + + num_frames = episode_length + for i in range(num_frames): + frame = { + "observation.state": state[i], + "action": action[i], + } + + for camera, img_array in cameras.items(): + frame[f"observation.images.{camera}"] = img_array[i] + + dataset.add_frame(frame, task=task) + + dataset.save_episode() + + return dataset + + +def json_to_lerobot( + raw_dir: Path, + repo_id: str, + robot_type: str, # e.g., Unitree_Z1_Single, Unitree_Z1_Dual, Unitree_G1_Dex1, Unitree_G1_Dex3, Unitree_G1_Brainco, Unitree_G1_Inspire + *, + push_to_hub: bool = False, + mode: Literal["video", "image"] = "video", + dataset_config: DatasetConfig = DEFAULT_DATASET_CONFIG, +): + if (HF_LEROBOT_HOME / repo_id).exists(): + shutil.rmtree(HF_LEROBOT_HOME / repo_id) + + dataset = create_empty_dataset( + repo_id, + robot_type=robot_type, + mode=mode, + has_effort=False, + has_velocity=False, + dataset_config=dataset_config, + ) + dataset = populate_dataset( + dataset, + raw_dir, + robot_type=robot_type, + ) + + if push_to_hub: + dataset.push_to_hub(upload_large_folder=True) + + +def local_push_to_hub( + repo_id: str, + root_path: Path, +): + dataset = LeRobotDataset(repo_id=repo_id, root=root_path) + dataset.push_to_hub(upload_large_folder=True) + + +if __name__ == "__main__": + tyro.cli(json_to_lerobot) diff --git a/utils/sort_and_rename_folders.py b/utils/sort_and_rename_folders.py new file mode 100644 index 000000000..6d018f59f --- /dev/null +++ b/utils/sort_and_rename_folders.py @@ -0,0 +1,40 @@ +""" +Script to convert Unitree json data to the LeRobot dataset v2.0 format. + +python unitree_lerobot/utils/sort_and_rename_folders.py --data_dir $HOME/datasets/g1_grabcube_double_hand +""" + +import os +import tyro +import uuid +from pathlib import Path + + +def sort_and_rename_folders(data_dir: Path) -> None: + # Get the list of folders sorted by name + folders = sorted([f for f in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, f))]) + + temp_mapping = {} + + # First, rename all folders to unique temporary names + for folder in folders: + temp_name = str(uuid.uuid4()) + original_path = os.path.join(data_dir, folder) + temp_path = os.path.join(data_dir, temp_name) + os.rename(original_path, temp_path) + temp_mapping[temp_name] = folder + + # Then, rename them to the final target names + start_number = 0 + for temp_name, original_folder in temp_mapping.items(): + new_folder_name = f"episode_{start_number:04d}" + temp_path = os.path.join(data_dir, temp_name) + new_path = os.path.join(data_dir, new_folder_name) + os.rename(temp_path, new_path) + start_number += 1 + + print("The folders have been successfully renamed.") + + +if __name__ == "__main__": + tyro.cli(sort_and_rename_folders) diff --git a/zmq_cameras_example.json b/zmq_cameras_example.json new file mode 100644 index 000000000..1ca314f80 --- /dev/null +++ b/zmq_cameras_example.json @@ -0,0 +1,13 @@ +[ + { + "name": "unitree_g1_head", + "address": "192.168.123.164", + "port": 5554 + }, + { + "name": "lab_camera_1", + "address": "10.0.0.100", + "port": 5555 + } +] +