745 lines
28 KiB
Python
745 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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Go1 RL Policy 推理 - Walk-These-Ways 预训练模型 + MuJoCo 可视化
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模型: body_latest.jit + adaptation_module_latest.jit (GRU-based policy with history)
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训练环境: IsaacGym (walk-these-ways)
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使用方法:
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python3 /home/8x54zj-m/unitree_mujoco/go1_walk_these_ways_inference.py
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键盘控制:
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W/S: 前进/后退
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A/D: 左转/右转
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Q/E: 侧向左移/右移
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R/F: 抬腿高度 高/低
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U/J: 身体前倾/后仰 (pitch)
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I/K: 身体右倾/左倾 (roll)
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空格: 停止
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ESC: 退出
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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 sys
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import threading
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import time
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import signal
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import collections
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import queue
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# ============================================================
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# 全局退出标志
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# ============================================================
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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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MODEL_DIR = "/home/8x54zj-m/walk-these-ways/runs/gait-conditioned-agility/pretrain-v0/train/025417.456545/checkpoints"
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BODY_MODEL_PATH = os.path.join(MODEL_DIR, "body_latest.jit")
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ADAPT_MODEL_PATH = os.path.join(MODEL_DIR, "adaptation_module_latest.jit")
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XML_PATH = "/home/8x54zj-m/unitree_mujoco/data/go1/xml/go1.xml"
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MESH_PATH = "/home/8x54zj-m/unitree_mujoco/data/go1/meshes"
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# ============================================================
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# Walk-These-Ways 模型参数 (来自 parameters.pkl & deploy配置)
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# ============================================================
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NUM_OBS = 70 # 单步观测维度
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NUM_OBS_HISTORY = 30 # 历史观测步数
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OBS_BUFFER_SIZE = NUM_OBS * NUM_OBS_HISTORY # 2100
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# 观测缩放 (来自 obs_scales)
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SCALE_LIN_VEL = 2.0
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SCALE_ANG_VEL = 0.25
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SCALE_DOF_POS = 1.0
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SCALE_DOF_VEL = 0.05
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# 动作缩放 (来自 control.action_scale)
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ACTION_SCALE = 0.25
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HIP_SCALE_REDUCTION = 0.5 # hip关节额外缩放 (来自 control.hip_scale_reduction)
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# 限幅 (来自 normalization)
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CLIP_OBSERVATIONS = 100.0
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CLIP_ACTIONS = 10.0
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# 默认关节角度 (来自 init_state.default_joint_angles, deploy顺序 FL, FR, RL, RR)
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# Mujoco 关节顺序是 FR, FL, RR, RL，所以需要映射
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# Mujoco: [FR_hip, FR_thigh, FR_calf, FL_hip, FL_thigh, FL_calf, RR_hip, RR_thigh, RR_calf, RL_hip, RL_thigh, RL_calf]
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# Deploy: [FL_hip, FL_thigh, FL_calf, FR_hip, FR_thigh, FR_calf, RL_hip, RL_thigh, RL_calf, RR_hip, RR_thigh, RR_calf]
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DEFAULT_DOF_POS_DEPLOY = np.array([
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0.1, 0.8, -1.5, # FL: hip, thigh, calf
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-0.1, 0.8, -1.5, # FR: hip, thigh, calf
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0.1, 1.0, -1.5, # RL: hip, thigh, calf
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-0.1, 1.0, -1.5, # RR: hip, thigh, calf
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], dtype=np.float32)
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# Mujoco 默认姿态 (用于初始化Mujoco仿真)
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DEFAULT_DOF_POS_MUJOCO = np.array([
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-0.1, 0.8, -1.5, # FR: hip, thigh, calf (Mujoco order)
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0.1, 0.8, -1.5, # FL: hip, thigh, calf
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-0.1, 1.0, -1.5, # RR: hip, thigh, calf
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0.1, 1.0, -1.5, # RL: hip, thigh, calf
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], dtype=np.float32)
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# IsaacGym/Deploy -> Mujoco 关节顺序映射
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DEPLOY_TO_MUJOCO_MAPPING = np.array([3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8])
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# 速度命令范围
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MAX_LIN_VEL = 1.0 # 最大线速度 m/s
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MAX_ANG_VEL = 1.0 # 与 deploy 一致
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# PD控制参数
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KP = 20.0
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KD = 0.1
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# MuJoCo 摩擦系数配置
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FRICTION = [0.6, 0.3, 0.3]
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FLOOR_FRICTION = [0.0, 0.0, 0.0] # 训练时地面摩擦为0
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# ============================================================
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# 步态预设 (与 deploy ElegantGaitProfile 一致)
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# ============================================================
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GAIT_PRESETS = {
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'trot': {
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'name': 'Trot (小跑)',
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'phase': 0.5, 'offset': 0.0, 'bound': 0.0, 'duration': 0.5,
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'description': '对角腿同步'
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},
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'pace': {
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'name': 'Pace (踱步)',
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'phase': 0.0, 'offset': 0.0, 'bound': 0.5, 'duration': 0.5,
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'description': '同侧腿同步'
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},
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'bound': {
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'name': 'Bound (奔跑)',
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'phase': 0.0, 'offset': 0.5, 'bound': 0.0, 'duration': 0.5,
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'description': '前后腿同步'
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},
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'pronk': {
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'name': 'Pronk (跳跃)',
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'phase': 0.0, 'offset': 0.0, 'bound': 0.0, 'duration': 0.5,
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'description': '四腿同时'
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},
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}
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current_gait = 'trot'
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# ============================================================
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# 键盘输入读取 (独立线程, pynput 事件驱动)
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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 (独立线程)")
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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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# 辅助函数
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# ============================================================
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def quaternion_to_rotation_matrix(q):
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"""四元数转旋转矩阵 (MuJoCo 格式: qx, qy, qz, qw)"""
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qx, qy, qz, qw = q
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norm = np.sqrt(qx**2 + qy**2 + qz**2 + qw**2)
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qx, qy, qz, qw = qx/norm, qy/norm, qz/norm, qw/norm
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return np.array([
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[1-2*(qy**2+qz**2), 2*(qx*qy-qz*qw), 2*(qx*qz+qy*qw)],
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[2*(qx*qy+qz*qw), 1-2*(qx**2+qz**2), 2*(qy*qz-qx*qw)],
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[2*(qx*qz-qy*qw), 2*(qy*qz+qx*qw), 1-2*(qx**2+qy**2)]
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])
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def quat_rotate_inverse(data, v):
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"""四元数逆旋转 (世界坐标系 -> 躯干坐标系)
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使用 data.xmat 直接计算，更可靠
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"""
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base_rot = data.xmat[1].reshape(3, 3) # body 1 = trunk
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return base_rot.T @ np.array(v, dtype=np.float64)
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def rotation_matrix_from_quat(data):
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"""从四元数计算旋转矩阵 (躯干坐标系)
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使用 MuJoCo 内置函数 mju_quat2Mat，保证与 MuJoCo 内部一致
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返回: 3x3 旋转矩阵 R, 使得 R * world_vec = body_vec
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"""
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quat_raw = data.qpos[3:7] # [x, y, z, w]
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quat_mju = np.array([quat_raw[3], quat_raw[0], quat_raw[1], quat_raw[2]], dtype=np.float64)
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R_mju = np.zeros(9, dtype=np.float64)
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mujoco.mju_quat2Mat(R_mju, quat_mju)
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return R_mju.reshape(3, 3)
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def grav_to_arrow(grav):
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"""将重力投影向量转为箭头字符串 (躯干坐标系视图)
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grav: [gx, gy, gz] 世界重力在躯干坐标系下的投影
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躯干坐标系: X=前, Y=左, Z=上
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返回: (world_arrow, body_arrow)
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"""
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# 世界重力: 始终是 (0, 0, -1) = 纯Z轴负方向 = 向下 ↓
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world_arrow = "↓"
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# 躯干坐标系重力投影
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gx, gy, gz = grav
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# 判断主体方向 (忽略很小分量)
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abs_x, abs_y, abs_z = abs(gx), abs(gy), abs(gz)
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# 标准化
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total = abs_x + abs_y + abs_z
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nx, ny, nz = gx / total, gy / total, gz / total
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# Z 分量判断上下
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if nz > 0.1:
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z_arrow = "↑"
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elif nz < -0.1:
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z_arrow = "↓"
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else:
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z_arrow = "·"
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# X 分量判断前后
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if nx > 0.1:
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x_arrow = "→"
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elif nx < -0.1:
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x_arrow = "←"
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else:
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x_arrow = "·"
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# Y 分量判断左右 (正Y=机器左=世界右→所以箭头反向)
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if ny > 0.1:
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y_arrow = "←" # 机器左倾
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elif ny < -0.1:
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y_arrow = "→" # 机器右倾
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else:
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y_arrow = "·"
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body_arrow = f"{x_arrow}{y_arrow}{z_arrow}"
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return world_arrow, body_arrow
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def compute_observations_wtw(data, prev_action, last_action, commands, clock_inputs, default_dof_pos_mujoco, obs_scales):
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"""
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计算 Walk-These-Ways 策略的观测向量 (70维)
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"""
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obs = np.zeros(NUM_OBS, dtype=np.float32)
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# 获取四元数 (MuJoCo 格式: x, y, z, w) -> 转换为 (w, x, y, z)
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quat_mujoco = data.qpos[3:7] # (x, y, z, w)
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quat = np.concatenate([quat_mujoco[3:4], quat_mujoco[0:3]])
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# 1. 重力投影到躯干坐标系 (使用单位重力 [0,0,-1])
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projected_gravity = quat_rotate_inverse(data, np.array([0., 0., -1.]))
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obs[0:3] = projected_gravity
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# 2. 命令 (已缩放)
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obs[3:18] = commands[:15]
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# 3. 相对关节位置 (Mujoco顺序 -> Deploy顺序)
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current_joint_pos = data.qpos[7:19]
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current_joint_pos = current_joint_pos[DEPLOY_TO_MUJOCO_MAPPING]
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default_dof_pos_deploy = default_dof_pos_mujoco[DEPLOY_TO_MUJOCO_MAPPING]
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dof_pos_rel = (current_joint_pos - default_dof_pos_deploy) * obs_scales['dof_pos']
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obs[18:30] = dof_pos_rel
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# 4. 关节速度 * scale (Mujoco顺序 -> Deploy顺序)
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joint_vel = data.qvel[6:18]
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joint_vel = joint_vel[DEPLOY_TO_MUJOCO_MAPPING]
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obs[30:42] = joint_vel * obs_scales['dof_vel']
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# 5. 上一步动作 (clipped)
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obs[42:54] = np.clip(prev_action, -CLIP_ACTIONS, CLIP_ACTIONS)
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# 6. 上上步动作
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obs[54:66] = np.clip(last_action, -CLIP_ACTIONS, CLIP_ACTIONS)
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# 7. 时钟输入
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obs[66:70] = clock_inputs
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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 compute_commands_scale():
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"""计算 commands_scale，与 deploy/lcm_agent.py 一致"""
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obs_scales = {
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'lin_vel': 2.0,
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'ang_vel': 0.25,
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'dof_pos': 1.0,
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'dof_vel': 0.05,
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'body_height_cmd': 2.0,
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'footswing_height_cmd': 0.15,
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'body_pitch_cmd': 0.3,
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'body_roll_cmd': 0.3,
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'stance_width_cmd': 1.0,
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'stance_length_cmd': 1.0,
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'aux_reward_cmd': 1.0,
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}
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commands_scale = np.array([
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obs_scales['lin_vel'], obs_scales['lin_vel'],
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obs_scales['ang_vel'], obs_scales['body_height_cmd'],
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1, 1, 1, 1, 1, # gait params (unscaled)
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obs_scales['footswing_height_cmd'],
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obs_scales['body_pitch_cmd'],
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obs_scales['body_roll_cmd'],
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obs_scales['stance_width_cmd'],
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obs_scales['stance_length_cmd'],
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obs_scales['aux_reward_cmd'],
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1, 1, 1, 1, 1 # padding
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], dtype=np.float32)
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return commands_scale[:15], obs_scales
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# ============================================================
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# 主程序
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# ============================================================
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def main():
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global current_gait
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import torch
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# 1. 加载 MuJoCo 模型
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os.chdir(MESH_PATH)
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with open(XML_PATH, 'r') as f:
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xml_content = f.read()
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xml_content = xml_content.replace('meshdir="../meshes/"', f'meshdir="{MESH_PATH}"')
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xml_content = xml_content.replace('<default>\n=', '<default>\n')
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# ---- 添加重力箭头 mocap body (橙色小球) ----
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# mocap body 会自动在viewer中渲染，无需手动管理geom
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grav_arrow_body = '''
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<body name="grav_arrow" pos="0 0 0" mocap="true">
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<geom type="sphere" size="0.04 0 0" rgba="1.0 0.5 0.0 0.8" contype="0" conaffinity="0" friction="0 0 0"/>
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</body>
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'''
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# 在 </worldbody> 之前插入
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xml_content = xml_content.replace('</worldbody>', grav_arrow_body + '</worldbody>')
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model = mujoco.MjModel.from_xml_string(xml_content)
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data = mujoco.MjData(model)
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# 验证 mocap body 添加成功
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grav_arrow_mocap_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, "grav_arrow")
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if grav_arrow_mocap_id < 0:
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print("[WARN] grav_arrow body not found in model!")
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else:
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print(f"[INFO] grav_arrow body id: {grav_arrow_mocap_id}, nmocap: {model.nmocap}")
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# mocap_pos[0] 对应第一个 mocap body（我们添加的 grav_arrow）
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GRAV_MOCAP_IDX = 0
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if model.nmocap < 1:
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print("[WARN] No mocap bodies in model! Gravity arrow disabled.")
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print(f"[INFO] MuJoCo Model: {model.nbody} bodies, {model.nq} DoF, {model.nu} actuators")
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print(f"[INFO] Mujoco关节顺序: {[mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_JOINT, i) for i in range(1, 13)]}")
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print(f"[INFO] Deploy关节顺序: FL, FR, RL, RR (与Mujoco不同)")
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# 2. 设置摩擦系数
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||
floor_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, "floor")
|
||
if floor_id >= 0:
|
||
model.geom_friction[floor_id] = FLOOR_FRICTION
|
||
print(f"[INFO] 地面摩擦系数设置为: {FLOOR_FRICTION}")
|
||
|
||
for i in range(model.ngeom):
|
||
if i != floor_id:
|
||
model.geom_friction[i] = FRICTION
|
||
print(f"[INFO] 机器人摩擦系数设置为: {FRICTION}")
|
||
|
||
# 3. 加载 Walk-These-Ways 模型
|
||
body_module = torch.jit.load(BODY_MODEL_PATH, map_location='cpu')
|
||
adapt_module = torch.jit.load(ADAPT_MODEL_PATH, map_location='cpu')
|
||
body_module.eval()
|
||
adapt_module.eval()
|
||
print(f"[INFO] Body model loaded from: {BODY_MODEL_PATH}")
|
||
print(f"[INFO] Adapt model loaded from: {ADAPT_MODEL_PATH}")
|
||
print(f"[INFO] 观测维度: {NUM_OBS}, 历史步数: {NUM_OBS_HISTORY}")
|
||
print(f"[INFO] Action scale: {ACTION_SCALE}, Hip scale reduction: {HIP_SCALE_REDUCTION}")
|
||
|
||
# 4. 计算 commands_scale
|
||
commands_scale, obs_scales = compute_commands_scale()
|
||
print(f"[INFO] Commands scale: {commands_scale}")
|
||
|
||
# 5. 初始化观测历史 buffer
|
||
obs_buffer = np.zeros(OBS_BUFFER_SIZE, dtype=np.float32) # 30 * 70 = 2100
|
||
|
||
# 6. 初始化机器人姿态
|
||
data.qpos[7:19] = DEFAULT_DOF_POS_MUJOCO
|
||
data.qvel[:] = 0.0
|
||
data.qpos[2] = 0.35 # 抬高躯干
|
||
mujoco.mj_step(model, data)
|
||
|
||
# ---- 用 MuJoCo 内置函数验证旋转矩阵 ----
|
||
quat_mj = data.qpos[3:7].copy() # [x,y,z,w]
|
||
print(f" [CHECK] MuJoCo qpos[3:7] = {quat_mj}")
|
||
# MuJoCo's own rotation matrix from quaternion
|
||
R_mj = np.zeros(9)
|
||
mujoco.mju_quat2Mat(R_mj, quat_mj)
|
||
R_mj = R_mj.reshape(3, 3)
|
||
print(f" [CHECK] MuJoCo mju_quat2Mat R (identity if q=[1,0,0,0]≈id): \n{R_mj.round(4)}")
|
||
mujoco.mj_step(model, data)
|
||
|
||
# 打印初始化后的姿态确认
|
||
quat_mj = data.qpos[3:7]
|
||
grav_init = quat_rotate_inverse(data, np.array([0., 0., -1.]))
|
||
R_init = rotation_matrix_from_quat(data)
|
||
quat_std = np.array([quat_mj[3], quat_mj[0], quat_mj[1], quat_mj[2]])
|
||
print(f" [INIT] quat(MuJoCo)={quat_mj} | quat_as_std={quat_std.round(3)} | grav_world→body={grav_init.round(3)} | trunk_z={data.qpos[2]:.3f}")
|
||
|
||
# ---- 3D 可视化: 躯干坐标系 vs 世界坐标系 ----
|
||
try:
|
||
import matplotlib.pyplot as plt
|
||
from mpl_toolkits.mplot3d import Axes3D
|
||
R_init = rotation_matrix_from_quat(data)
|
||
fig = plt.figure(figsize=(6, 6))
|
||
ax = fig.add_subplot(111, projection='3d')
|
||
# 世界坐标系 (黑色)
|
||
ax.quiver(0, 0, 0, 1.2, 0, 0, color='k', linewidth=1.5, arrow_length_ratio=0.1)
|
||
ax.quiver(0, 0, 0, 0, 1.2, 0, color='k', linewidth=1.5, arrow_length_ratio=0.1)
|
||
ax.quiver(0, 0, 0, 0, 0, 1.2, color='k', linewidth=1.5, arrow_length_ratio=0.1)
|
||
ax.text(1.3, 0, 0, "Xw(前)", fontsize=9)
|
||
ax.text(0, 1.3, 0, "Yw(左)", fontsize=9)
|
||
ax.text(0, 0, 1.3, "Zw(上)", fontsize=9)
|
||
# 躯干坐标系 (彩色)
|
||
body_x = R_init[:, 0] * 0.8 # R第一列 = body X轴在世界
|
||
body_y = R_init[:, 1] * 0.8 # R第二列 = body Y轴在世界
|
||
body_z = R_init[:, 2] * 0.8 # R第三列 = body Z轴在世界
|
||
ax.quiver(0, 0, 0, *body_x, color='r', linewidth=2, arrow_length_ratio=0.1)
|
||
ax.quiver(0, 0, 0, *body_y, color='g', linewidth=2, arrow_length_ratio=0.1)
|
||
ax.quiver(0, 0, 0, *body_z, color='b', linewidth=2, arrow_length_ratio=0.1)
|
||
ax.text(body_x[0]*1.2, body_x[1]*1.2, body_x[2]*1.2, "Xb(前)", color='r', fontsize=9)
|
||
ax.text(body_y[0]*1.2, body_y[1]*1.2, body_y[2]*1.2, "Yb(左)", color='g', fontsize=9)
|
||
ax.text(body_z[0]*1.2, body_z[1]*1.2, body_z[2]*1.2, "Zb(上)", color='b', fontsize=9)
|
||
# 重力向量
|
||
grav_arrow = grav_init * 0.6
|
||
ax.quiver(0, 0, 0, *grav_arrow, color='orange', linewidth=2.5, arrow_length_ratio=0.1)
|
||
ax.text(grav_arrow[0]*1.2, grav_arrow[1]*1.2, grav_arrow[2]*1.2, f"g={grav_init.round(2)}", color='orange', fontsize=9)
|
||
ax.set_xlim([-1.5, 1.5]); ax.set_ylim([-1.5, 1.5]); ax.set_zlim([-1.5, 1.5])
|
||
ax.set_xlabel("X (世界)"); ax.set_ylabel("Y (世界)"); ax.set_zlabel("Z (世界)")
|
||
ax.set_title("躯干坐标系 (RGB=XYZ轴) vs 世界坐标系 (K) | 橙色=重力投影")
|
||
plt.tight_layout()
|
||
plt.savefig("/home/8x54zj-m/unitree_mujoco/body_frame_axes.png", dpi=150)
|
||
print(f" [INIT] 坐标系可视化已保存: body_frame_axes.png")
|
||
print(f" [INIT] 旋转矩阵 R (body←world, 列=body轴在world中):\n{R_init.round(3)}")
|
||
plt.close()
|
||
except Exception as e:
|
||
print(f" [WARN] 可视化失败: {e}")
|
||
|
||
# 初始化历史 (填充零)
|
||
for _ in range(NUM_OBS_HISTORY):
|
||
dummy_obs = np.zeros(NUM_OBS, dtype=np.float32)
|
||
obs_buffer = np.concatenate([obs_buffer[NUM_OBS:], dummy_obs])
|
||
|
||
# 7. 初始化键盘控制
|
||
keyboard_reader = KeyboardReader()
|
||
keyboard_reader.init()
|
||
print("[INFO] 键盘控制已启用!")
|
||
print(" W/S: 前进/后退 (max 1.0 m/s)")
|
||
print(" A/D: 左转/右转 (max 5.0 rad/s)")
|
||
print(" Q/E: 侧向左移/右移")
|
||
print(" R/F: 抬腿高度 高/低")
|
||
print(" U/J: 身体前倾/后仰")
|
||
print(" I/K: 身体右倾/左倾")
|
||
print(" 1: Trot (小跑) 2: Pace (踱步)")
|
||
print(" 3: Bound (奔跑) 4: Pronk (跳跃)")
|
||
print(" 空格: 停止")
|
||
print(" ESC: 退出")
|
||
print(f"[INFO] 当前步态: {GAIT_PRESETS[current_gait]['name']} - {GAIT_PRESETS[current_gait]['description']}")
|
||
|
||
# 8. 启动交互式查看器
|
||
import mujoco.viewer as mv
|
||
view = mv.launch_passive(model, data)
|
||
print("[INFO] 交互式查看器已启动!")
|
||
|
||
# 9. 主循环
|
||
step_count = 0
|
||
last_inference_time = time.time()
|
||
inference_interval = 0.02 # 50Hz
|
||
|
||
# 默认命令
|
||
x_vel_cmd = 0.0
|
||
y_vel_cmd = 0.0
|
||
yaw_vel_cmd = 0.0
|
||
body_height_cmd = 0.0
|
||
footswing_height_cmd = 0.15
|
||
body_pitch_cmd = 0.0
|
||
body_roll_cmd = 0.0
|
||
gait_frequency_cmd = 3.0
|
||
gait_phase_cmd = GAIT_PRESETS[current_gait]['phase']
|
||
gait_offset_cmd = GAIT_PRESETS[current_gait]['offset']
|
||
gait_bound_cmd = GAIT_PRESETS[current_gait]['bound']
|
||
gait_duration_cmd = GAIT_PRESETS[current_gait]['duration']
|
||
|
||
prev_action = np.zeros(12, dtype=np.float32)
|
||
last_action = np.zeros(12, dtype=np.float32)
|
||
gait_indices = 0.0
|
||
|
||
clock_inputs = np.zeros(4, dtype=np.float32)
|
||
|
||
print("[INFO] 开始 RL 策略推理...")
|
||
|
||
while view.is_running() and not g_exit_requested:
|
||
current_time = time.time()
|
||
|
||
# 空格: 停止
|
||
if keyboard_reader.is_key_pressed(' '):
|
||
x_vel_cmd = 0.0
|
||
y_vel_cmd = 0.0
|
||
yaw_vel_cmd = 0.0
|
||
|
||
# 步态切换
|
||
gait_keys = {'1': 'trot', '2': 'pace', '3': 'bound', '4': 'pronk'}
|
||
for key, gait_name in gait_keys.items():
|
||
if keyboard_reader.is_key_pressed(key):
|
||
if current_gait != gait_name:
|
||
current_gait = gait_name
|
||
gait_phase_cmd = GAIT_PRESETS[gait_name]['phase']
|
||
gait_offset_cmd = GAIT_PRESETS[gait_name]['offset']
|
||
gait_bound_cmd = GAIT_PRESETS[gait_name]['bound']
|
||
gait_duration_cmd = GAIT_PRESETS[gait_name]['duration']
|
||
print(f"[INFO] 切换步态: {GAIT_PRESETS[gait_name]['name']} - {GAIT_PRESETS[gait_name]['description']}")
|
||
|
||
# 连续动作
|
||
if keyboard_reader.is_key_held('w'):
|
||
x_vel_cmd = MAX_LIN_VEL
|
||
elif keyboard_reader.is_key_held('s'):
|
||
x_vel_cmd = -MAX_LIN_VEL
|
||
else:
|
||
x_vel_cmd = 0.0
|
||
|
||
if keyboard_reader.is_key_held('q'):
|
||
y_vel_cmd = MAX_LIN_VEL
|
||
elif keyboard_reader.is_key_held('e'):
|
||
y_vel_cmd = -MAX_LIN_VEL
|
||
else:
|
||
y_vel_cmd = 0.0
|
||
|
||
if keyboard_reader.is_key_held('a'):
|
||
yaw_vel_cmd = MAX_ANG_VEL
|
||
elif keyboard_reader.is_key_held('d'):
|
||
yaw_vel_cmd = -MAX_ANG_VEL
|
||
else:
|
||
yaw_vel_cmd = 0.0
|
||
|
||
# 抬腿高度
|
||
if keyboard_reader.is_key_held('r'):
|
||
footswing_height_cmd = min(0.35, footswing_height_cmd + 0.005)
|
||
elif keyboard_reader.is_key_held('f'):
|
||
footswing_height_cmd = max(0.03, footswing_height_cmd - 0.005)
|
||
|
||
# 身体前后倾斜
|
||
if keyboard_reader.is_key_held('u'):
|
||
body_pitch_cmd = min(0.4, body_pitch_cmd + 0.01)
|
||
elif keyboard_reader.is_key_held('j'):
|
||
body_pitch_cmd = max(-0.4, body_pitch_cmd - 0.01)
|
||
|
||
# 身体左右倾斜
|
||
if keyboard_reader.is_key_held('i'):
|
||
body_roll_cmd = min(0.0, body_roll_cmd + 0.01)
|
||
elif keyboard_reader.is_key_held('k'):
|
||
body_roll_cmd = max(0.0, body_roll_cmd - 0.01)
|
||
|
||
# ESC: 退出
|
||
if keyboard_reader.is_key_pressed('escape'):
|
||
print("[INFO] ESC pressed, exiting...")
|
||
break
|
||
|
||
# 50Hz 推理
|
||
if current_time - last_inference_time >= inference_interval:
|
||
# 构建原始命令 (15维)
|
||
raw_commands = np.zeros(15, dtype=np.float32)
|
||
raw_commands[0] = x_vel_cmd
|
||
raw_commands[1] = y_vel_cmd
|
||
raw_commands[2] = yaw_vel_cmd
|
||
raw_commands[3] = body_height_cmd
|
||
raw_commands[4] = gait_frequency_cmd
|
||
raw_commands[5] = gait_phase_cmd
|
||
raw_commands[6] = gait_offset_cmd
|
||
raw_commands[7] = gait_bound_cmd
|
||
raw_commands[8] = gait_duration_cmd
|
||
raw_commands[9] = footswing_height_cmd
|
||
raw_commands[10] = body_pitch_cmd
|
||
raw_commands[11] = body_roll_cmd
|
||
raw_commands[12] = 0.25 # stance_width
|
||
raw_commands[13] = 0.4 # stance_length
|
||
|
||
# 应用 commands_scale
|
||
commands = raw_commands * commands_scale
|
||
|
||
# 更新 gait indices (每 policy step = 4 * sim_dt = 4 * 0.005 = 0.02)
|
||
gait_indices += 0.02 * gait_frequency_cmd
|
||
if gait_indices > 1.0:
|
||
gait_indices -= 1.0
|
||
|
||
# 计算 clock_inputs
|
||
phase = gait_phase_cmd
|
||
offset = gait_offset_cmd
|
||
bound = gait_bound_cmd
|
||
foot_indices = [
|
||
gait_indices + phase + offset + bound, # FL
|
||
gait_indices + offset, # FR
|
||
gait_indices + bound, # RL
|
||
gait_indices + phase # RR
|
||
]
|
||
clock_inputs[0] = np.sin(2 * np.pi * foot_indices[0])
|
||
clock_inputs[1] = np.sin(2 * np.pi * foot_indices[1])
|
||
clock_inputs[2] = np.sin(2 * np.pi * foot_indices[2])
|
||
clock_inputs[3] = np.sin(2 * np.pi * foot_indices[3])
|
||
|
||
# 计算当前步观测
|
||
obs = compute_observations_wtw(
|
||
data, prev_action, last_action, commands, clock_inputs, DEFAULT_DOF_POS_MUJOCO, obs_scales
|
||
)
|
||
|
||
# 更新观测历史 buffer
|
||
obs_buffer = np.concatenate([obs_buffer[NUM_OBS:], obs])
|
||
|
||
# 准备模型输入
|
||
obs_buffer_tensor = torch.from_numpy(obs_buffer).float().unsqueeze(0)
|
||
obs_tensor = torch.from_numpy(obs).float().unsqueeze(0)
|
||
|
||
# 推理
|
||
with torch.inference_mode():
|
||
latent = adapt_module(obs_buffer_tensor)
|
||
combined_input = torch.cat([obs_buffer_tensor, latent], dim=1)
|
||
action = body_module(combined_input).numpy().flatten()
|
||
|
||
# 动作后处理
|
||
action = np.clip(action, -CLIP_ACTIONS, CLIP_ACTIONS)
|
||
|
||
# 保存动作历史
|
||
last_action = prev_action.copy()
|
||
prev_action = action.copy()
|
||
|
||
last_inference_time = current_time
|
||
|
||
# ---- PD 控制 ----
|
||
action_scaled = prev_action * ACTION_SCALE
|
||
|
||
# hip关节额外缩放
|
||
hip_indices = [0, 3, 6, 9]
|
||
for i in hip_indices:
|
||
action_scaled[i] *= HIP_SCALE_REDUCTION
|
||
|
||
joint_targets_mujoco = action_scaled[DEPLOY_TO_MUJOCO_MAPPING] + DEFAULT_DOF_POS_MUJOCO
|
||
|
||
current_pos = data.qpos[7:19]
|
||
current_vel = data.qvel[6:18]
|
||
torque = KP * (joint_targets_mujoco - current_pos) - KD * current_vel
|
||
|
||
data.ctrl[:] = torque
|
||
|
||
mujoco.mj_step(model, data)
|
||
|
||
# grav_proj: 世界重力 [0,0,-1] 变换到躯干坐标系
|
||
grav_proj = quat_rotate_inverse(data, np.array([0., 0., -1.]))
|
||
|
||
# ---- 重力箭头可视化: 橙色小球始终指向世界下方 ----
|
||
torso_pos = data.xpos[1] # body 1 = trunk
|
||
arrow_pos = torso_pos + np.array([0., 0., -1.]) * 0.15
|
||
|
||
# ---- 画橙色小球: 用 mocap body 更新位置 ----
|
||
if grav_arrow_mocap_id >= 0:
|
||
data.mocap_pos[GRAV_MOCAP_IDX] = arrow_pos.astype(np.float64)
|
||
|
||
view.sync()
|
||
|
||
step_count += 1
|
||
if step_count % 100 == 0:
|
||
trunk_z = data.qpos[2]
|
||
lin_vel = np.linalg.norm(data.qvel[0:3])
|
||
quat_mj = data.qpos[3:7]
|
||
quat = np.concatenate([quat_mj[3:4], quat_mj[0:3]])
|
||
grav_proj = quat_rotate_inverse(data, np.array([0., 0., -1.]))
|
||
quat_raw = data.qpos[3:7]
|
||
quat_mju = np.array([quat_raw[3], quat_raw[0], quat_raw[1], quat_raw[2]], dtype=np.float64)
|
||
R_mju = np.zeros(9, dtype=np.float64)
|
||
mujoco.mju_quat2Mat(R_mju, quat_mju)
|
||
R = R_mju.reshape(3, 3)
|
||
world_a, body_a = grav_to_arrow(grav_proj)
|
||
gait_short = {'trot': 'TROT', 'pace': 'PACE', 'bound': 'BOUND', 'pronk': 'PRONK'}
|
||
# R 的三列 = body X/Y/Z 在世界坐标系中的方向
|
||
print(f" Step {step_count}: quat={quat_raw.round(3)} grav={grav_proj.round(2)}{body_a} | "
|
||
f"R=[{R[0,:].round(2)}, {R[1,:].round(2)}, {R[2,:].round(2)}] | "
|
||
f"trunk_z={trunk_z:.3f}m, vel={lin_vel:.3f}m/s")
|
||
|
||
keyboard_reader.restore()
|
||
view.close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|