265 lines
11 KiB
Python
265 lines
11 KiB
Python
#!/usr/bin/env python3
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"""DreamWaQ MuJoCo sim2sim — VAE encoder + Actor, 5-frame history buffer.
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Usage:
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uv run scripts/dreamwaq_sim2sim_mujoco.py # flat
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uv run scripts/dreamwaq_sim2sim_mujoco.py --terrain rough
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uv run scripts/dreamwaq_sim2sim_mujoco.py --onnx path/to/policy.onnx
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Controls:
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W/S: forward/back Q/E: left/right A/D: rotate
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Space: stop R: reset Esc: quit
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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 onnxruntime as ort
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import os, sys, threading, queue, argparse, time, signal
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g_exit_requested = False
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signal.signal(signal.SIGINT, lambda *a: globals().update(g_exit_requested=True))
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# ═══════════════════════════════════════════════════════════════════════
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_PROJECT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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XML_DIR = os.path.join(_PROJECT, "motrix_envs", "src", "motrix_envs", "locomotion", "go1", "xmls")
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DEFAULT_ONNX = os.path.join(_PROJECT, "exports_go1_dreamwaq", "policy.onnx")
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# ── DreamWaQ params (matching training: PD 28/0.7, action_scale 0.25, ctrl_dt=0.02) ──
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NUM_OBS = 45
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NUM_ACTIONS = 12
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HISTORY_LEN = 5
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ACTION_SCALE = 0.25
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KP = 28.0
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KD = 0.7
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CLIP_ACTIONS = 23.7
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CLIP_OBS = 100.0
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MAX_VX, MAX_VY, MAX_WZ = 1.0, 1.0, 1.0
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# DreamWaQ default joint angles — MUST match MuJoCo XML joint order:
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# qpos[7:19] = 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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DEFAULT_ANGLES = np.array([
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-0.1, 0.8, -1.5, # FR
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0.1, 0.8, -1.5, # FL
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-0.1, 1.0, -1.5, # RR
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0.1, 1.0, -1.5, # RL
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], dtype=np.float32)
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# ═══════════════════════════════════════════════════════════════════════
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# Keyboard
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# ═══════════════════════════════════════════════════════════════════════
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from pynput import keyboard
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class KB:
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def __init__(self):
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self._q = queue.Queue(); self.running = True
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self.held = set(); self._t = None; self._l = None
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def _n(self, k):
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try:
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if hasattr(k, 'char') and k.char: return k.char.lower()
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except: pass
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return str(k).lower()
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def _w(self):
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while self.running:
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try:
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et, k = self._q.get(timeout=0.05)
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n = self._n(k)
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if et == 'press': self.held.add(n)
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elif et == 'release': self.held.discard(n)
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except queue.Empty: pass
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def init(self):
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self._l = keyboard.Listener(on_press=lambda k: self._q.put(('press', k)),
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on_release=lambda k: self._q.put(('release', k)))
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self._l.start()
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self._t = threading.Thread(target=self._w, daemon=True); self._t.start()
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def stop(self): self.running = False; self._l.stop()
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# ═══════════════════════════════════════════════════════════════════════
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# Sensor
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# ═══════════════════════════════════════════════════════════════════════
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def get_sensor(m, d, name):
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sid = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_SENSOR, name)
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if sid < 0: return None
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adr = m.sensor_adr[sid]; dim = m.sensor_dim[sid]
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return d.sensordata[adr:adr+dim].copy()
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def compute_obs(model, data, commands, last_action):
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"""DreamWaQ observation (Manaro-Alpha order):
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ang_vel(3) + gravity(3) + commands(3) + joint_pos(12) + joint_vel(12) + actions(12) = 45
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"""
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obs = np.zeros(NUM_OBS, dtype=np.float32)
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# ang_vel [0:3]
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g = get_sensor(model, data, "gyro")
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obs[0:3] = (g if g is not None else data.qvel[3:6]) * 0.25
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# gravity [3:6] (read from MuJoCo model, matching training)
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grav_world = model.opt.gravity.copy()
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grav_world = grav_world / np.linalg.norm(grav_world) # normalize
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R = data.xmat[1].reshape(3, 3)
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obs[3:6] = (R.T @ grav_world).astype(np.float32)
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# commands [6:9]
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obs[6:9] = commands * np.array([2.0, 2.0, 0.25], dtype=np.float32)
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# joint_pos [9:21]
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obs[9:21] = (data.qpos[7:19] - DEFAULT_ANGLES) * 1.0
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# joint_vel [21:33]
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obs[21:33] = data.qvel[6:18] * 0.05
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# last_action [33:45]
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obs[33:45] = last_action
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return np.clip(obs, -CLIP_OBS, CLIP_OBS)
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# ═══════════════════════════════════════════════════════════════════════
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# Main
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# ═══════════════════════════════════════════════════════════════════════
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def main():
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p = argparse.ArgumentParser()
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p.add_argument("--onnx", default=DEFAULT_ONNX)
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p.add_argument("--terrain", default="flat", choices=["flat", "rough", "stairs", "dreamwaq", "stairs_test", "stairs_box", "flat_stairs"])
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p.add_argument("--level", type=int, default=0, help="terrain difficulty level 0-9 (0=flat, 9=hardest)")
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args = p.parse_args()
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# Select XML scene
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terrain_map = {
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"flat": "scene_motor_actuator.xml",
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"rough": "scene_rough_terrain.xml",
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"stairs": "scene_stairs_terrain.xml",
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"dreamwaq": "scene_dreamwaq_terrain.xml",
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"stairs_test": "scene_stairs_test.xml",
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"stairs_box": "scene_stairs_box.xml",
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"flat_stairs": "scene_flat_stairs.xml",
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}
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xml_file = os.path.join(XML_DIR, terrain_map[args.terrain])
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if not os.path.exists(args.onnx):
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print(f"[ERROR] ONNX not found: {args.onnx}")
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print("Run: uv run scripts/export_dreamwaq_onnx.py (after training completes)")
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sys.exit(1)
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os.chdir(XML_DIR)
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with open(xml_file) as f:
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model = mujoco.MjModel.from_xml_string(f.read())
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data = mujoco.MjData(model)
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# Spawn pose. Hfield heights: MuJoCo z = gp[2] + sbase + (hd * ztop).
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# The stairs_test terrain has sbase=0, flat platform z=0; just lift by clearance.
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if args.terrain == "flat_stairs":
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lvl = max(0, min(1, args.level))
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col = np.random.randint(0, 4)
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spawn_y = 4.0 - lvl * 8.0 # level 0 flat at y=+4, level 1 stairs at y=-4
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spawn_x = -12.0 + col * 8.0 # platform center (cell center x)
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elif args.terrain == "stairs_test":
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spawn_x, spawn_y = -7.5, -4.0 # flat approach before first step (1m zone)
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elif args.terrain == "stairs_box":
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spawn_x, spawn_y = -2.0, 0.0 # flat ground before stairs
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elif args.terrain == "dreamwaq":
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lvl = max(0, min(9, args.level))
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col = np.random.randint(0, 4) # NUM_COLS=4
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spawn_y = 36.0 - lvl * 8.0 # level 0 flat at y=+36, level 9 stairs at y=-36
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spawn_x = -12.0 + col * 8.0 + 4.0 # centre of cell
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print(f"[Level {lvl}] type={col} spawn=({spawn_x:.1f}, {spawn_y:.1f})")
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else:
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spawn_x, spawn_y = (0.0, 0.0)
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# When DISPLAY is a virtual framebuffer (Xvfb), MuJoCo headless rendering is
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# handled transparently; on a real display this opens a normal GUI window.
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# No explicit headless flag needed — MuJoCo glfw detects the display type.
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def hfield_z(mx, my):
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gid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_GEOM, "floor")
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if gid < 0 or model.geom_type[gid] != mujoco.mjtGeom.mjGEOM_HFIELD:
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return 0.0
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hf = model.geom_dataid[gid]
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nrow, ncol = int(model.hfield_nrow[hf]), int(model.hfield_ncol[hf])
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sx, sy, ztop, sbase = model.hfield_size[hf]
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adr = model.hfield_adr[hf]
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hd = model.hfield_data[adr:adr + nrow * ncol].reshape(nrow, ncol)
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gp = model.geom_pos[gid]
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col = int(np.clip(((mx - gp[0]) / sx * 0.5 + 0.5) * (ncol - 1), 0, ncol - 1))
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row = int(np.clip(((my - gp[1]) / sy * 0.5 + 0.5) * (nrow - 1), 0, nrow - 1))
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return float(gp[2] + sbase + hd[row, col] * ztop)
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spawn_z = hfield_z(spawn_x, spawn_y) + 0.45 # standing clearance above terrain
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def reset_state():
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data.qpos[:] = 0
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data.qpos[0:3] = [spawn_x, spawn_y, spawn_z]; data.qpos[3:7] = [1, 0, 0, 0]
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data.qpos[7:19] = DEFAULT_ANGLES; data.qvel[:] = 0
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mujoco.mj_forward(model, data)
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reset_state()
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# ONNX (2 inputs: observations + obs_history)
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session = ort.InferenceSession(args.onnx, providers=['CPUExecutionProvider'])
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print(f"[DreamWaQ] {args.onnx}")
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print(f"[Terrain] {args.terrain}")
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print(f"[CTRL] W/S前后 Q/E左右 A/D旋转 Space停 R重置 Esc退出")
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kb = KB(); kb.init()
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view = viewer.launch_passive(model, data)
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# Camera tracking: follow the trunk body
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trunk_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, "trunk")
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view.cam.lookat = data.body(trunk_id).xpos.copy()
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view.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING
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view.cam.trackbodyid = trunk_id
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step = 0
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vx, vy, wz = 0.0, 0.0, 0.0
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last_action = np.zeros(NUM_ACTIONS, dtype=np.float32)
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action = np.zeros(NUM_ACTIONS, dtype=np.float32)
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history = np.zeros((1, HISTORY_LEN, NUM_OBS), dtype=np.float32)
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decimation = 4 # MuJoCo dt=0.005, policy dt=0.02 (DreamWaQ aligned)
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loop_t0 = time.time()
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while view.is_running() and not g_exit_requested:
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keys = kb.held
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if 'escape' in keys: break
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if 'r' in keys:
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reset_state()
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last_action[:] = 0; history[:] = 0
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print("[R] Reset")
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if ' ' in keys: vx = vy = wz = 0.0
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vx = MAX_VX if 'w' in keys else (-MAX_VX if 's' in keys else 0.0)
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vy = MAX_VY if 'q' in keys else (-MAX_VY if 'e' in keys else 0.0)
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wz = MAX_WZ if 'a' in keys else (-MAX_WZ if 'd' in keys else 0.0)
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if step % decimation == 0:
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cmd = np.array([vx, vy, wz], dtype=np.float32)
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obs = compute_obs(model, data, cmd, last_action)
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# Shift history + add new obs
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history = np.concatenate([history[:, 1:, :], obs.reshape(1, 1, -1)], axis=1)
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# ONNX inference
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outputs = session.run(None, {
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'obs': obs.reshape(1, -1).astype(np.float32),
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'obs_history': history.reshape(1, -1).astype(np.float32),
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})
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action = outputs[0][0]
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action = np.clip(action, -CLIP_ACTIONS, CLIP_ACTIONS)
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last_action = action.copy()
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# PD control
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target = DEFAULT_ANGLES + action * ACTION_SCALE
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torques = KP * (target - data.qpos[7:19]) - KD * data.qvel[6:18]
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data.ctrl[:] = np.clip(torques, -CLIP_ACTIONS, CLIP_ACTIONS)
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mujoco.mj_step(model, data)
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view.sync()
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# Time sync (policy at 50Hz = 0.02s per step)
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expected = step * 0.02
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elapsed = time.time() - loop_t0
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if elapsed < expected:
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time.sleep(expected - elapsed)
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step += 1
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kb.stop(); view.close()
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if __name__ == "__main__":
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main()
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