download policy from the hub in examples/unitree_g1/gr00t_locomotion

examples/unitree_g1/gr00t_locomotion.py

@@ -7,6 +7,7 @@ This example demonstrates the NEW pattern for loading GR00T policies externally
 and passing them to the robot class.
 """
 
+import argparse
 import logging
 import threading
 import time
@@ -15,6 +16,7 @@ from collections import deque
 import numpy as np
 import onnxruntime as ort
 import torch
+from huggingface_hub import hf_hub_download
 
 from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
 from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
@@ -72,15 +74,32 @@ DOF_VEL_SCALE: float = 0.05
 CMD_SCALE: list = [2.0, 2.0, 0.25]
 
 
-def load_groot_policies() -> tuple:
-    """Load GR00T dual-policy system (Balance + Walk) from ONNX files."""
-    logger.info("Loading GR00T dual-policy system...")
+DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
+
+
+def load_groot_policies(
+    repo_id: str = DEFAULT_GROOT_REPO_ID,
+) -> tuple[ort.InferenceSession, ort.InferenceSession]:
+    """Load GR00T dual-policy system (Balance + Walk) from Hugging Face Hub.
+
+    Args:
+        repo_id: Hugging Face Hub repository ID containing the ONNX policies.
+    """
+    logger.info(f"Loading GR00T dual-policy system from Hugging Face Hub ({repo_id})...")
+
+    # Download ONNX policies from Hugging Face Hub
+    balance_path = hf_hub_download(
+        repo_id=repo_id,
+        filename="GR00T-WholeBodyControl-Balance.onnx",
+    )
+    walk_path = hf_hub_download(
+        repo_id=repo_id,
+        filename="GR00T-WholeBodyControl-Walk.onnx",
+    )
 
     # Load ONNX policies
-    policy_balance = ort.InferenceSession(
-        "examples/unitree_g1/locomotion/GR00T-WholeBodyControl-Balance.onnx"
-    )
-    policy_walk = ort.InferenceSession("examples/unitree_g1/locomotion/GR00T-WholeBodyControl-Walk.onnx")
+    policy_balance = ort.InferenceSession(balance_path)
+    policy_walk = ort.InferenceSession(walk_path)
 
     logger.info("GR00T policies loaded successfully")
 
@@ -99,7 +118,6 @@ class GrootLocomotionController:
     """
 
     def __init__(self, policy_balance, policy_walk, robot, config):
-
         self.policy_balance = policy_balance
         self.policy_walk = policy_walk
         self.robot = robot
@@ -128,7 +146,6 @@ class GrootLocomotionController:
         logger.info("GrootLocomotionController initialized")
 
     def groot_locomotion_run(self):
-
         # get current observation
         robot_state = self.robot.get_observation()
 
@@ -150,15 +167,14 @@ class GrootLocomotionController:
             self.robot.remote_controller.rx = 0.0
             self.robot.remote_controller.ry = 0.0
 
-        self.locomotion_cmd[0] = self.robot.remote_controller.ly # forward/backward
-        self.locomotion_cmd[1] = self.robot.remote_controller.lx * -1 # left/right
-        self.locomotion_cmd[2] = self.robot.remote_controller.rx * -1 # rotation rate
+        self.locomotion_cmd[0] = self.robot.remote_controller.ly  # forward/backward
+        self.locomotion_cmd[1] = self.robot.remote_controller.lx * -1  # left/right
+        self.locomotion_cmd[2] = self.robot.remote_controller.rx * -1  # rotation rate
 
         for i in range(29):
             self.groot_qj_all[i] = robot_state.motor_state[i].q
             self.groot_dqj_all[i] = robot_state.motor_state[i].dq
 
-
         # adapt observation for g1_23dof
         for idx in MISSING_JOINTS:
             self.groot_qj_all[idx] = 0.0
@@ -173,12 +189,11 @@ class GrootLocomotionController:
         ang_vel = np.array(robot_state.imu_state.gyroscope, dtype=np.float32)
         gravity_orientation = self.robot.get_gravity_orientation(quat)
 
-        #scale joint positions and velocities before policy inference
+        # scale joint positions and velocities before policy inference
         qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
         dqj_obs = dqj_obs * DOF_VEL_SCALE
         ang_vel_scaled = ang_vel * ANG_VEL_SCALE
 
-
         # build single frame observation
         self.groot_obs_single[:3] = self.locomotion_cmd * np.array(CMD_SCALE)
         self.groot_obs_single[3] = self.groot_height_cmd
@@ -202,7 +217,7 @@ class GrootLocomotionController:
         obs_tensor = torch.from_numpy(self.groot_obs_stacked).unsqueeze(0)
 
         cmd_magnitude = np.linalg.norm(self.locomotion_cmd)
-        
+
         if cmd_magnitude < 0.05:
             # balance/standing policy for small commands
             selected_policy = self.policy_balance
@@ -218,7 +233,7 @@ class GrootLocomotionController:
         # transform action back to target joint positions
         target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * LOCOMOTION_ACTION_SCALE
 
-        # command motors 
+        # command motors
         for i in range(15):
             motor_idx = i
             self.robot.msg.motor_cmd[motor_idx].q = target_dof_pos_15[i]
@@ -235,7 +250,7 @@ class GrootLocomotionController:
             self.robot.msg.motor_cmd[joint_idx].kd = self.robot.kd[joint_idx]
             self.robot.msg.motor_cmd[joint_idx].tau = 0
 
-        #send action to robot
+        # send action to robot
         self.robot.send_action(self.robot.msg)
 
     def _locomotion_thread_loop(self):
@@ -298,7 +313,9 @@ class GrootLocomotionController:
             alpha = i / num_step
             for motor_idx in range(dof_size):
                 target_pos = default_pos[motor_idx]
-                self.robot.msg.motor_cmd[motor_idx].q = init_dof_pos[motor_idx] * (1 - alpha) + target_pos * alpha
+                self.robot.msg.motor_cmd[motor_idx].q = (
+                    init_dof_pos[motor_idx] * (1 - alpha) + target_pos * alpha
+                )
                 self.robot.msg.motor_cmd[motor_idx].qd = 0
                 self.robot.msg.motor_cmd[motor_idx].kp = self.robot.kp[motor_idx]
                 self.robot.msg.motor_cmd[motor_idx].kd = self.robot.kd[motor_idx]
@@ -308,16 +325,25 @@ class GrootLocomotionController:
             time.sleep(self.robot.control_dt)
         logger.info("Reached default position (legs only)")
 
-if __name__ == "__main__":
-    
-    #load policies
-    policy_balance, policy_walk = load_groot_policies()
 
-    #initialize robot
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="GR00T Locomotion Controller for Unitree G1")
+    parser.add_argument(
+        "--repo-id",
+        type=str,
+        default=DEFAULT_GROOT_REPO_ID,
+        help=f"Hugging Face Hub repo ID for GR00T policies (default: {DEFAULT_GROOT_REPO_ID})",
+    )
+    args = parser.parse_args()
+
+    # load policies
+    policy_balance, policy_walk = load_groot_policies(repo_id=args.repo_id)
+
+    # initialize robot
     config = UnitreeG1Config()
     robot = UnitreeG1(config)
 
-    #initialize gr00t locomotion controller
+    # initialize gr00t locomotion controller
     groot_controller = GrootLocomotionController(
         policy_balance=policy_balance,
         policy_walk=policy_walk,
@@ -325,20 +351,20 @@ if __name__ == "__main__":
         config=config,
     )
 
-    #reset legs and start locomotion thread
+    # reset legs and start locomotion thread
     groot_controller.reset_robot()
     groot_controller.start_locomotion_thread()
 
-    #log status
+    # log status
     logger.info("Robot initialized with GR00T locomotion policies")
     logger.info("Locomotion controller running in background thread")
     logger.info("Press Ctrl+C to stop")
 
-    #keep robot alive
+    # keep robot alive
     try:
         while True:
             time.sleep(1.0)
     except KeyboardInterrupt:
         print("\nStopping locomotion...")
         groot_controller.stop_locomotion_thread()
-        print("Done!")
+        print("Done!")