372 lines
12 KiB
Python
372 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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MOTRIXLAB_UNTRIEE_GO1_SIM2SIM
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source /opt/mujoco/venv/bin/activate
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cd /opt/unitree_mujoco
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python demo/go1_sim2sim_mujoco.py
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"""
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import numpy as np
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import mujoco
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from mujoco import viewer
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import os
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import threading
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import signal
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import queue
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import argparse
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import time
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g_exit_requested = False
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def signal_handler(signum, frame):
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global g_exit_requested
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g_exit_requested = True
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signal.signal(signal.SIGINT, signal_handler)
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# ============================================================
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# 配置
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# ============================================================
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_PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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DEFAULT_ONNX_PATH = os.path.join(_PROJECT_DIR, "exports_go1_flat", "policy.onnx")
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MOTRIX_XML_DIR = os.path.join(_PROJECT_DIR, "motrix_envs", "src", "motrix_envs", "locomotion", "go1", "xmls")
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XML_PATH = f"{MOTRIX_XML_DIR}/go1_motor_actuator.xml"
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TERRAIN = "none"
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# ============================================================
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# MotrixLab 参数 (来自 cfg.py)
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# ============================================================
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NUM_OBS = 45 # 去掉线速度观测 (原来是48)
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NUM_ACTIONS = 12
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OBS_SCALES = {'lin_vel': 2.0, 'ang_vel': 0.25, 'dof_pos': 1.0, 'dof_vel': 0.05}
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ACTION_SCALE = 0.05
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KP = 80.0
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KD = 1.0
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CLIP_ACTIONS = 23.7
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CLIP_OBSERVATIONS = 100.0
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MAX_LIN_VEL_X = 1.0
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MAX_LIN_VEL_Y = 1.0
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MAX_ANG_VEL = 1.0 # 匹配训练时的角速度命令范围 [-1.0, 1.0]
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# ============================================================
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# 关节名称和顺序
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# ============================================================
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POLICY_JOINT_NAMES = [
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"FR_hip", "FR_thigh", "FR_calf",
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"FL_hip", "FL_thigh", "FL_calf",
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"RR_hip", "RR_thigh", "RR_calf",
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"RL_hip", "RL_thigh", "RL_calf",
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]
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DEFAULT_JOINT_ANGLES = np.array([
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-0.0, 0.9, -1.8, # FR_hip, FR_thigh, FR_calf
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0.0, 0.9, -1.8, # FL_hip, FL_thigh, FL_calf
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-0.0, 0.9, -1.8, # RR_hip, RR_thigh, RR_calf
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0.0, 0.9, -1.8, # RL_hip, RL_thigh, RL_calf
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], dtype=np.float32)
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MUJOCO_TO_POLICY = np.arange(12, dtype=np.int64)
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POLICY_TO_MUJOCO = np.arange(12, dtype=np.int64)
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# ============================================================
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# 键盘输入
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# ============================================================
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from pynput import keyboard
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class KeyboardReader:
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def __init__(self):
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self._event_queue = queue.Queue()
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self.running = True
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self.shared_keys_held = set()
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self.shared_one_shot = set()
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self._reader_thread = None
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self._listener = None
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def _normalize_key(self, key):
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try:
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if hasattr(key, 'char') and key.char is not None:
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return key.char.lower()
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except:
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pass
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key_str = str(key)
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if key_str == 'Key.esc':
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return 'escape'
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elif key_str == 'Key.space':
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return 'space'
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elif key_str.startswith('Key.'):
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return key_str.lower()
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return key_str.lower()
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def _reader_worker(self):
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while self.running:
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try:
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event_type, key = self._event_queue.get(timeout=0.05)
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k = self._normalize_key(key)
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if event_type == 'press':
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self.shared_keys_held.add(k)
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self.shared_one_shot.discard(k)
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elif event_type == 'release':
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self.shared_keys_held.discard(k)
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self.shared_one_shot.discard(k)
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except queue.Empty:
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pass
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def init(self):
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def on_press(key):
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self._event_queue.put(('press', key))
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def on_release(key):
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self._event_queue.put(('release', key))
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try:
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self._listener = keyboard.Listener(on_press=on_press, on_release=on_release)
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self._listener.start()
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self._reader_thread = threading.Thread(target=self._reader_worker, daemon=True)
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self._reader_thread.start()
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print("[INFO] 键盘监听已启动")
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except Exception as e:
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print(f"[WARN] 无法初始化键盘监听: {e}")
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def is_key_pressed(self, key):
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k = self._normalize_key(key) if isinstance(key, str) else self._normalize_key(key)
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if k not in self.shared_keys_held or k in self.shared_one_shot:
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return False
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self.shared_one_shot.add(k)
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return True
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def is_key_held(self, key):
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k = self._normalize_key(key) if isinstance(key, str) else self._normalize_key(key)
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return k in self.shared_keys_held
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def restore(self):
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self.running = False
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if self._listener:
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self._listener.stop()
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# ============================================================
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# Sensor 读取
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# ============================================================
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def get_sensor(model, data, name):
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sid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, name)
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if sid < 0:
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return None
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adr = model.sensor_adr[sid]
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dim = model.sensor_dim[sid]
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return data.sensordata[adr:adr + dim].copy()
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def compute_observations_motrix(model, data, commands, last_actions):
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"""计算 45 维观测 (去掉局部线速度，策略仅靠命令+关节信息+陀螺仪来推理)
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布局 (45 dims):
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[0:3] 陀螺仪 (ang_vel * 0.25)
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[3:6] 重力向量 (躯干坐标系，无缩放)
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[6:18] 关节位置偏差 (dof_pos * 1.0)
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[18:30] 关节速度 (dof_vel * 0.05)
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[30:42] 上一步动作 (原始值)
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[42:45] 命令 [vx*2.0, vy*2.0, wz*0.25]
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"""
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obs = np.zeros(NUM_OBS, dtype=np.float32)
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# 陀螺仪
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gyro = get_sensor(model, data, "gyro")
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if gyro is not None:
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obs[0:3] = gyro * OBS_SCALES['ang_vel']
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else:
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obs[0:3] = data.qvel[3:6] * OBS_SCALES['ang_vel']
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# 重力向量 (躯干坐标系)
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base_rot = data.xmat[1].reshape(3, 3)
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gravity_world = np.array([0., 0., -1.], dtype=np.float64)
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local_gravity = base_rot.T @ gravity_world
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obs[3:6] = local_gravity.astype(np.float32)
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# 关节位置偏差
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joint_pos = data.qpos[7:19]
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dof_pos_rel = (joint_pos - DEFAULT_JOINT_ANGLES) * OBS_SCALES['dof_pos']
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obs[6:18] = dof_pos_rel
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# 关节速度
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joint_vel = data.qvel[6:18]
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obs[18:30] = joint_vel * OBS_SCALES['dof_vel']
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# 上一步动作
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obs[30:42] = last_actions
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# 命令
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obs[42:45] = commands * np.array([OBS_SCALES['lin_vel'], OBS_SCALES['lin_vel'], OBS_SCALES['ang_vel']], dtype=np.float32)
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# 限幅
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obs = np.clip(obs, -CLIP_OBSERVATIONS, CLIP_OBSERVATIONS)
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return obs
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def main():
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import re
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import onnxruntime as ort
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parser = argparse.ArgumentParser(description="MotrixLab Go1 Policy Inference in MuJoCo")
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parser.add_argument("--onnx", type=str, default=DEFAULT_ONNX_PATH)
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parser.add_argument("--terrain", type=str, default=TERRAIN, choices=["none", "rough", "stairs"])
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args = parser.parse_args()
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os.chdir(MOTRIX_XML_DIR)
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if args.terrain == "rough":
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xml_file = f"{MOTRIX_XML_DIR}/scene_rough_terrain.xml"
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elif args.terrain == "stairs":
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xml_file = f"{MOTRIX_XML_DIR}/scene_stairs_terrain.xml"
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else:
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xml_file = f"{MOTRIX_XML_DIR}/scene_motor_actuator.xml" # flat floor
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with open(xml_file, 'r') as f:
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xml_content = f.read()
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model = mujoco.MjModel.from_xml_string(xml_content)
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data = mujoco.MjData(model)
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print(f"[INFO] Model: {model.nbody} bodies, {model.nq} DoF, {model.nu} actuators")
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print(f"[INFO] MuJoCo timestep: {model.opt.timestep}")
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print(f"[INFO] 关节顺序 (qpos[7:19]): {[mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_JOINT, i) for i in range(1, 13)]}")
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for sensor_name in ["gyro", "local_linvel"]:
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sid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SENSOR, sensor_name)
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print(f"[SENSOR] {sensor_name}: {'存在' if sid >= 0 else '不存在'}")
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# 初始化
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data.qpos[0:3] = np.array([0.0, 0.0, 0.42])
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data.qpos[3:7] = np.array([1.0, 0.0, 0.0, 0.0]) # 默认四元数
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data.qpos[7:19] = DEFAULT_JOINT_ANGLES
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data.qvel[:] = 0.0
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data.ctrl[:] = 0.0
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mujoco.mj_forward(model, data)
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print(f"[INIT] qpos[2]={data.qpos[2]:.3f}")
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print(f"[INIT] qpos[7:19]={data.qpos[7:19]}")
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print(f"[INIT] DEFAULT_JOINT_ANGLES={DEFAULT_JOINT_ANGLES}")
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# 加载onnx
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session = ort.InferenceSession(args.onnx, providers=['CPUExecutionProvider'])
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print(f"[INFO] loaded")
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# 主循环
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ctrl_dt = 0.01 # 100Hz
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num_steps_per_inference = int(ctrl_dt / model.opt.timestep)
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print(f"[INFO] 每 {num_steps_per_inference} 步推理一次 ")
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step_count = 0
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inference_step = 0
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x_vel_cmd = 0.0
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y_vel_cmd = 0.0
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yaw_vel_cmd = 0.0
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commands = np.array([x_vel_cmd, y_vel_cmd, yaw_vel_cmd], dtype=np.float32)
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last_actions = np.zeros(NUM_ACTIONS, dtype=np.float32)
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action = np.zeros(NUM_ACTIONS, dtype=np.float32)
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# 键盘
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keyboard_reader = KeyboardReader()
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keyboard_reader.init()
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view = viewer.launch_passive(model, data)
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print("[INFO] 已启动!")
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loop_start_time = time.time()
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while view.is_running() and not g_exit_requested:
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# 键盘命令
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if keyboard_reader.is_key_pressed(' '):
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x_vel_cmd = 0.0
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y_vel_cmd = 0.0
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yaw_vel_cmd = 0.0
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if keyboard_reader.is_key_held('w'):
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x_vel_cmd = MAX_LIN_VEL_X
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elif keyboard_reader.is_key_held('s'):
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x_vel_cmd = -MAX_LIN_VEL_X
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else:
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x_vel_cmd = 0.0
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if keyboard_reader.is_key_held('q'):
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y_vel_cmd = MAX_LIN_VEL_Y
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elif keyboard_reader.is_key_held('e'):
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y_vel_cmd = -MAX_LIN_VEL_Y
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else:
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y_vel_cmd = 0.0
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if keyboard_reader.is_key_held('a'):
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yaw_vel_cmd = MAX_ANG_VEL
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elif keyboard_reader.is_key_held('d'):
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yaw_vel_cmd = -MAX_ANG_VEL
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else:
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yaw_vel_cmd = 0.0
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if keyboard_reader.is_key_pressed('r'):
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# 重置机器人到初始位置
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data.qpos[0:3] = np.array([0.0, 0.0, 0.42])
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data.qpos[3:7] = np.array([1.0, 0.0, 0.0, 0.0])
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data.qpos[7:19] = DEFAULT_JOINT_ANGLES
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data.qvel[:] = 0.0
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data.ctrl[:] = 0.0
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last_actions = np.zeros(NUM_ACTIONS, dtype=np.float32)
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mujoco.mj_forward(model, data)
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print("[RESET] 机器人已重置")
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if keyboard_reader.is_key_pressed('escape'):
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break
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# 推理 (每 N 步一次)
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if inference_step == 0:
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commands[0] = x_vel_cmd
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commands[1] = y_vel_cmd
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commands[2] = yaw_vel_cmd
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obs = compute_observations_motrix(model, data, commands, last_actions)
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# 推理
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action = session.run(None, {'observations': obs.reshape(1, -1).astype(np.float32)})[0][0]
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action = np.clip(action, -CLIP_ACTIONS, CLIP_ACTIONS)
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last_actions = action.copy()
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# PD 控制
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# joint_targets = action * action_scale + default_angles
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joint_targets = DEFAULT_JOINT_ANGLES + action * ACTION_SCALE
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current_pos = data.qpos[7:19]
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current_vel = data.qvel[6:18]
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torques = KP * (joint_targets - current_pos) - KD * current_vel
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torques = np.clip(torques, -CLIP_ACTIONS, CLIP_ACTIONS)
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data.ctrl[:] = torques
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mujoco.mj_step(model, data)
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view.sync()
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expected_time = step_count * ctrl_dt
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elapsed = time.time() - loop_start_time
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sleep_time = expected_time - elapsed
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if sleep_time > 0:
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time.sleep(sleep_time)
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step_count += 1
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inference_step = (inference_step + 1) % num_steps_per_inference
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if step_count % 200 == 0:
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trunk_z = data.qpos[2]
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lin_vel = np.linalg.norm(data.qvel[0:3])
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print(f"\n========== Step {step_count} ==========")
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print(f"[CMD] x={x_vel_cmd:.2f}, y={y_vel_cmd:.2f}, yaw={yaw_vel_cmd:.2f}")
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print(f"[OBS] gyro={obs[0:3]}, grav={obs[3:6]}")
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print(f"[ACTION] raw={action[:4]}... scaled={action[:4]*ACTION_SCALE}...")
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print(f"[TARGET] {joint_targets[:4]}...")
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print(f"[TORQUE] {torques[:4]}...")
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print(f"[STATE] z={trunk_z:.3f}m, vel={lin_vel:.3f}m/s")
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print(f"==========================================\n")
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keyboard_reader.restore()
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view.close()
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if __name__ == "__main__":
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main()
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