minimal maniskill cpu step

gradio-web/minimal_maniskill_cpu_step.py

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+"""Minimal ManiSkill CPU sim/render sanity check.
+
+This uses an official ManiSkill environment instead of RoboMME wrappers so the
+execution path stays as small as possible.
+"""
+
+from __future__ import annotations
+
+import argparse
+import warnings
+
+import gymnasium as gym
+import mani_skill.envs  # noqa: F401 - registers ManiSkill environments
+
+
+warnings.filterwarnings(
+    "ignore",
+    message=r"CUDA reports that you have .* fork_rng",
+    category=UserWarning,
+)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env-id", default="PickCube-v1")
+    parser.add_argument("--seed", type=int, default=0)
+    args = parser.parse_args()
+
+    env = gym.make(
+        args.env_id,
+        obs_mode="rgbd",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        sim_backend="physx_cpu",
+        render_backend="sapien_cpu",
+    )
+
+    try:
+        obs, info = env.reset(seed=args.seed)
+        print(f"reset ok: env_id={args.env_id}")
+        print(f"obs keys: {list(obs.keys())}")
+        print(f"info keys: {list(info.keys())}")
+
+        action = env.action_space.sample()
+        obs, reward, terminated, truncated, info = env.step(action)
+
+        rgb = obs["sensor_data"]["base_camera"]["rgb"]
+        depth = obs["sensor_data"]["base_camera"]["depth"]
+
+        print("step ok")
+        print(f"reward={reward}")
+        print(f"terminated={terminated}")
+        print(f"truncated={truncated}")
+        print(f"rgb shape={tuple(rgb.shape)} dtype={rgb.dtype}")
+        print(f"depth shape={tuple(depth.shape)} dtype={depth.dtype}")
+        print(f"info keys after step: {list(info.keys())}")
+    finally:
+        env.close()
+
+
+if __name__ == "__main__":
+    main()