mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-24 18:26:11 +00:00
Steven Palma
206 lines
7.4 KiB
Python
206 lines
7.4 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from collections import deque
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import numpy as np
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import onnxruntime as ort
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from huggingface_hub import hf_hub_download
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from .g1_utils import (
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REMOTE_AXES,
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REMOTE_BUTTONS,
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G1_29_JointIndex,
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get_gravity_orientation,
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)
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logger = logging.getLogger(__name__)
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GROOT_DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
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GROOT_DEFAULT_ANGLES[[0, 6]] = -0.1 # Hip pitch
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GROOT_DEFAULT_ANGLES[[3, 9]] = 0.3 # Knee
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GROOT_DEFAULT_ANGLES[[4, 10]] = -0.2 # Ankle pitch
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# Control parameters
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ACTION_SCALE = 0.25
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CONTROL_DT = 0.02 # 50Hz
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ANG_VEL_SCALE: float = 0.25
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DOF_POS_SCALE: float = 1.0
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DOF_VEL_SCALE: float = 0.05
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CMD_SCALE: list[float] = [2.0, 2.0, 0.25]
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DEFAULT_GROOT_REPO_ID = "nepyope/GR00T-WholeBodyControl_g1"
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def load_groot_policies(
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repo_id: str = DEFAULT_GROOT_REPO_ID,
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) -> tuple[ort.InferenceSession, ort.InferenceSession]:
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"""Load GR00T dual-policy system (Balance + Walk) from the hub.
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Args:
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repo_id: Hugging Face Hub repository ID containing the ONNX policies.
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"""
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logger.info(f"Loading GR00T dual-policy system from the hub ({repo_id})...")
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# Download ONNX policies from Hugging Face Hub
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balance_path = hf_hub_download(
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repo_id=repo_id,
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filename="GR00T-WholeBodyControl-Balance.onnx",
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)
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walk_path = hf_hub_download(
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repo_id=repo_id,
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filename="GR00T-WholeBodyControl-Walk.onnx",
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)
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# Load ONNX policies
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policy_balance = ort.InferenceSession(balance_path)
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policy_walk = ort.InferenceSession(walk_path)
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logger.info("GR00T policies loaded successfully")
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return policy_balance, policy_walk
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class GrootLocomotionController:
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"""GR00T lower-body locomotion controller for the Unitree G1."""
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control_dt = CONTROL_DT # Expose for unitree_g1.py
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def __init__(self):
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# Load policies
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self.policy_balance, self.policy_walk = load_groot_policies()
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self.cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32) # vx, vy, theta_dot
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# Robot state
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self.groot_qj_all = np.zeros(29, dtype=np.float32)
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self.groot_dqj_all = np.zeros(29, dtype=np.float32)
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self.groot_action = np.zeros(15, dtype=np.float32)
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self.groot_obs_single = np.zeros(86, dtype=np.float32)
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self.groot_obs_history = deque(maxlen=6)
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self.groot_obs_stacked = np.zeros(516, dtype=np.float32)
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self.groot_height_cmd = 0.74 # Default base height
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self.groot_orientation_cmd = np.array([0.0, 0.0, 0.0], dtype=np.float32)
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# Input to GR00T is 6 frames (6*86D=516)
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for _ in range(6):
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self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
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logger.info("GrootLocomotionController initialized")
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def reset(self) -> None:
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"""Reset internal state for a new episode."""
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self.cmd[:] = 0.0
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self.groot_qj_all[:] = 0.0
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self.groot_dqj_all[:] = 0.0
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self.groot_action[:] = 0.0
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self.groot_obs_single[:] = 0.0
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self.groot_obs_stacked[:] = 0.0
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self.groot_height_cmd = 0.74
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self.groot_orientation_cmd[:] = 0.0
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self.groot_obs_history.clear()
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for _ in range(6):
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self.groot_obs_history.append(np.zeros(86, dtype=np.float32))
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def run_step(self, action: dict, lowstate) -> dict:
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"""Run one step of the locomotion controller.
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Args:
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action: Action dict containing remote.lx/ly/rx/ry and buttons
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lowstate: Robot lowstate containing motor positions/velocities and IMU
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Returns:
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Action dict for lower body joints (0-14)
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"""
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if lowstate is None:
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return {}
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buttons = [int(action.get(k, 0)) for k in REMOTE_BUTTONS]
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if buttons[0]: # R1 - raise waist
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self.groot_height_cmd += 0.001
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self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
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if buttons[4]: # R2 - lower waist
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self.groot_height_cmd -= 0.001
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self.groot_height_cmd = np.clip(self.groot_height_cmd, 0.50, 1.00)
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lx, ly, rx, _ry = (action.get(k, 0.0) for k in REMOTE_AXES)
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self.cmd[0] = ly # Forward/backward
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self.cmd[1] = -lx # Left/right (negated)
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self.cmd[2] = -rx # Rotation rate (negated)
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# Get joint positions and velocities from lowstate
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for motor in G1_29_JointIndex:
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idx = motor.value
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self.groot_qj_all[idx] = lowstate.motor_state[idx].q
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self.groot_dqj_all[idx] = lowstate.motor_state[idx].dq
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# Scale joint positions and velocities
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qj_obs = self.groot_qj_all.copy()
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dqj_obs = self.groot_dqj_all.copy()
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# Express IMU data in gravity frame of reference
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quat = lowstate.imu_state.quaternion
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ang_vel = np.array(lowstate.imu_state.gyroscope, dtype=np.float32)
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gravity_orientation = get_gravity_orientation(quat)
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# Scale joint positions and velocities before policy inference
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qj_obs = (qj_obs - GROOT_DEFAULT_ANGLES) * DOF_POS_SCALE
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dqj_obs = dqj_obs * DOF_VEL_SCALE
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ang_vel_scaled = ang_vel * ANG_VEL_SCALE
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# Build single frame observation
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self.groot_obs_single[:3] = self.cmd * np.array(CMD_SCALE)
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self.groot_obs_single[3] = self.groot_height_cmd
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self.groot_obs_single[4:7] = self.groot_orientation_cmd
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self.groot_obs_single[7:10] = ang_vel_scaled
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self.groot_obs_single[10:13] = gravity_orientation
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self.groot_obs_single[13:42] = qj_obs
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self.groot_obs_single[42:71] = dqj_obs
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self.groot_obs_single[71:86] = self.groot_action # 15D previous actions
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# Add to history and stack observations (6 frames × 86D = 516D)
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self.groot_obs_history.append(self.groot_obs_single.copy())
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# Stack all 6 frames into 516D vector
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for i, obs_frame in enumerate(self.groot_obs_history):
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start_idx = i * 86
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end_idx = start_idx + 86
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self.groot_obs_stacked[start_idx:end_idx] = obs_frame
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cmd_magnitude = np.linalg.norm(self.cmd)
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selected_policy = (
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self.policy_balance if cmd_magnitude < 0.05 else self.policy_walk
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) # Balance/standing policy for small commands, walking policy for movement commands
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# Run policy inference
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ort_inputs = {selected_policy.get_inputs()[0].name: np.expand_dims(self.groot_obs_stacked, axis=0)}
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ort_outs = selected_policy.run(None, ort_inputs)
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self.groot_action = ort_outs[0].squeeze()
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# Transform action back to target joint positions
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target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
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# Build action dict
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action_dict = {}
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for i in range(15):
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motor_name = G1_29_JointIndex(i).name
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action_dict[f"{motor_name}.q"] = float(target_dof_pos_15[i])
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return action_dict
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