File size: 4,475 Bytes
c1a4584 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | #!/usr/bin/env python3
"""Play the physical G1 + table + object env and write an MP4.
This does **not** load DexYCB, HaWoR, or ``isaaclab_replay.npz``. The scene is
procedural Isaac Lab: Nucleus G1 (minimal USD), a cuboid table, a cuboid object.
Actions are zeros (or ``--random``) unless you pass a later checkpoint.
./play.sh --headless --steps 300
# MP4: simulation/runs/rl/play/g1_table_object.mp4
"""
from __future__ import annotations
import argparse
import os
import sys
from pathlib import Path
from isaaclab.app import AppLauncher
parser = argparse.ArgumentParser(description="Play V2D-G1-TableObject and record MP4.")
parser.add_argument("--task", type=str, default="V2D-G1-TableObject-Play-v0")
parser.add_argument("--num_envs", type=int, default=1)
parser.add_argument("--steps", type=int, default=300)
parser.add_argument("--random", action="store_true")
parser.add_argument(
"--output",
type=Path,
default=None,
help="MP4 path (default: $RUNS_DIR/rl/play/g1_table_object.mp4)",
)
parser.add_argument("--fps", type=float, default=30.0)
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
args_cli.enable_cameras = True
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import gymnasium as gym # noqa: E402
import numpy as np # noqa: E402
import torch # noqa: E402
from isaaclab_tasks.utils import parse_env_cfg # noqa: E402
import v2d_sim # noqa: E402, F401
def _as_uint8_rgb(img) -> np.ndarray | None:
if img is None:
return None
if hasattr(img, "cpu"):
img = img.detach().cpu().numpy()
img = np.asarray(img)
if img.ndim == 4:
img = img[0]
img = img[..., :3]
if img.dtype != np.uint8:
scale = 255.0 if float(np.nanmax(img)) <= 1.5 else 1.0
img = np.clip(img * scale, 0, 255).astype(np.uint8)
h, w = img.shape[:2]
return img[: h - (h % 2), : w - (w % 2)]
def _write_mp4(path: Path, frames: list[np.ndarray], fps: float) -> None:
if not frames:
raise RuntimeError("no frames captured")
path.parent.mkdir(parents=True, exist_ok=True)
try:
import imageio.v2 as imageio
imageio.mimsave(str(path), frames, fps=float(fps), codec="libx264", pixelformat="yuv420p")
return
except Exception as exc:
print(f"[v2d-rl] imageio h264 failed ({exc}); trying OpenCV", flush=True)
import cv2
h, w = frames[0].shape[:2]
writer = cv2.VideoWriter(str(path), cv2.VideoWriter_fourcc(*"mp4v"), float(fps), (w, h))
if not writer.isOpened():
raise RuntimeError(f"could not open VideoWriter for {path}")
for fr in frames:
writer.write(cv2.cvtColor(fr, cv2.COLOR_RGB2BGR))
writer.release()
def main() -> None:
runs = Path(os.environ.get("RUNS_DIR", Path.cwd() / "runs"))
out_mp4 = (args_cli.output or (runs / "rl" / "play" / "g1_table_object.mp4")).resolve()
env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs)
env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array")
env.reset()
print("[v2d-rl] play uses procedural Isaac assets (not DexYCB / isaaclab_replay.npz)", flush=True)
print(" robot: Unitree G1 (G1_MINIMAL_CFG), gravity on, floating base", flush=True)
print(" table: kinematic cuboid object: dynamic cuboid", flush=True)
print(f" actions: {'random' if args_cli.random else 'zeros'} steps={args_cli.steps}", flush=True)
print(f" mp4 {out_mp4}", flush=True)
frames: list[np.ndarray] = []
for i in range(args_cli.steps):
if not simulation_app.is_running():
break
if args_cli.random:
action = torch.as_tensor(env.action_space.sample(), device=env.unwrapped.device)
else:
action = torch.zeros(env.unwrapped.action_space.shape, device=env.unwrapped.device)
_obs, rew, _term, _trunc, _info = env.step(action)
rgb = _as_uint8_rgb(env.unwrapped.render())
if rgb is not None:
frames.append(rgb)
if i % 50 == 0:
r = rew.mean().item() if hasattr(rew, "mean") else float(rew)
print(f" step {i} reward_mean={r:.3f} frames={len(frames)}", flush=True)
_write_mp4(out_mp4, frames, args_cli.fps)
print(f"[v2d-rl] wrote {out_mp4} ({len(frames)} frames)", flush=True)
env.close()
if __name__ == "__main__":
try:
main()
finally:
simulation_app.close()
sys.exit(0)
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