Merge branch 'main' into feat/depth-integration

docs/source/pi0fast.mdx

@@ -96,7 +96,7 @@ lerobot-train \
     --policy.type=pi0_fast \
     --output_dir=./outputs/pi0fast_training \
     --job_name=pi0fast_training \
-    --policy.pretrained_path=lerobot/pi0_fast_base \
+    --policy.pretrained_path=lerobot/pi0fast-base \
     --policy.dtype=bfloat16 \
     --policy.gradient_checkpointing=true \
     --policy.chunk_size=10 \
@@ -187,7 +187,7 @@ lerobot-train \
   --dataset.repo_id=lerobot/libero \
   --output_dir=outputs/libero_pi0fast \
   --job_name=libero_pi0fast \
-  --policy.path=lerobot/pi0fast_base \
+  --policy.path=lerobot/pi0fast-base \
   --policy.dtype=bfloat16 \
   --steps=100000 \
   --save_freq=20000 \

pyproject.toml

@@ -140,7 +140,14 @@ av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 # NOTE: 0.9.16 links against liburdfdom_sensor.so.4, which is unavailable on Ubuntu 24.04
 # (noble ships urdfdom 3.x). Cap below 0.9.16 until system urdfdom 4.x is broadly available.
-placo-dep = ["placo>=0.9.6,<0.9.16"]
+#
+# NOTE: placo pulls in pin (Pinocchio), whose binary wheels dlopen specific cmeel sonames
+# (liburdfdom_sensor.so.4.0, libtinyxml2.so.10) but declare only `>=` floors on their cmeel
+# packages. The 2026-05-21 major bumps (cmeel-urdfdom 6.0.0 -> .so.6, cmeel-tinyxml2 11.0.0
+# -> .so.11) ship newer sonames, so left unpinned the resolver grabs them and `import placo`
+# fails at load with "liburdfdom_sensor.so.4.0: cannot open shared object file" (see #3755).
+# There is no cmeel-urdfdom 5.x; <5 selects the 4.x ABI the placo/pin wheels are built against.
+placo-dep = ["placo>=0.9.6,<0.9.16", "cmeel-urdfdom>=4,<5", "cmeel-tinyxml2<11"]
 transformers-dep = ["transformers>=5.4.0,<5.6.0"]
 grpcio-dep = ["grpcio>=1.73.1,<2.0.0", "protobuf>=6.31.1,<8.0.0"]
 accelerate-dep = ["accelerate>=1.14.0,<2.0.0"]

src/lerobot/configs/default.py

@@ -82,8 +82,17 @@ class EvalConfig:
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     # Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
     use_async_envs: bool = True
+    # Whether to record eval rollouts as a LeRobot dataset on disk.
+    recording: bool = False
+    # If set, push recorded eval datasets to the Hub under this repo id (one repo per task,
+    # suffixed by task and env index). Requires recording=true.
+    recording_repo_id: str | None = None
+    # Whether the pushed recording repositories should be private.
+    recording_private: bool = False
 
     def __post_init__(self) -> None:
+        if self.recording_repo_id is not None and not self.recording:
+            raise ValueError("eval.recording_repo_id requires eval.recording=true.")
         if self.batch_size == 0:
             self.batch_size = self._auto_batch_size()
         if self.batch_size > self.n_episodes:

src/lerobot/scripts/lerobot_eval.py

@@ -72,8 +72,9 @@ from termcolor import colored
 from torch import Tensor, nn
 from tqdm import trange
 
-from lerobot.configs import parser
+from lerobot.configs import FeatureType, parser
 from lerobot.configs.eval import EvalPipelineConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.envs import (
     check_env_attributes_and_types,
     close_envs,
@@ -84,7 +85,7 @@ from lerobot.envs import (
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
-from lerobot.utils.constants import ACTION, DONE, OBS_STR, REWARD
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.io_utils import write_video
@@ -95,6 +96,65 @@ from lerobot.utils.utils import (
 )
 
 
+def _env_features_to_dataset_features(env_features: dict) -> dict:
+    """Convert EnvConfig.features to the dict format expected by LeRobotDataset.create()."""
+    features = {}
+    for key, ft in env_features.items():
+        shape = tuple(ft.shape)
+        if ft.type is FeatureType.VISUAL:
+            features[key] = {"dtype": "video", "shape": shape, "names": ["height", "width", "channel"]}
+        else:
+            features[key] = {"dtype": "float32", "shape": shape, "names": None}
+    features["next.reward"] = {"dtype": "float32", "shape": (1,), "names": None}
+    features["next.success"] = {"dtype": "bool", "shape": (1,), "names": None}
+    features["next.done"] = {"dtype": "bool", "shape": (1,), "names": None}
+    return features
+
+
+def _build_raw_frame(
+    raw_obs: dict,
+    env_idx: int,
+    action: np.ndarray,
+    reward: float,
+    success: bool,
+    done: bool,
+    task: str,
+    env_features: dict,
+) -> dict:
+    """Build a dataset frame from raw env observations for one env index.
+
+    Keys in the frame match the keys in env_features so they align with the
+    dataset schema created by _env_features_to_dataset_features().
+    """
+    frame: dict[str, Any] = {}
+    for key in env_features:
+        if key == ACTION:
+            continue
+        if key.startswith("next."):
+            continue
+        if "pixels" in raw_obs and isinstance(raw_obs["pixels"], dict):
+            for cam_name, img in raw_obs["pixels"].items():
+                candidate = f"{OBS_IMAGES}.{cam_name}"
+                if candidate == key:
+                    frame[key] = img[env_idx]
+            if key in frame:
+                continue
+        if "pixels" in raw_obs and not isinstance(raw_obs["pixels"], dict) and key in ("pixels", OBS_IMAGE):
+            frame[key] = raw_obs["pixels"][env_idx]
+            continue
+        if key in raw_obs and isinstance(raw_obs[key], np.ndarray):
+            val = raw_obs[key][env_idx]
+            if val.dtype == np.float64:
+                val = val.astype(np.float32)
+            frame[key] = val
+    frame[ACTION] = action
+    frame["next.reward"] = np.atleast_1d(np.float32(reward))
+    frame["next.success"] = np.atleast_1d(np.bool_(success))
+    frame["next.done"] = np.atleast_1d(np.bool_(done))
+    frame["task"] = task
+    return frame
+
+
 def rollout(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,
@@ -105,6 +165,10 @@ def rollout(
     seeds: list[int] | None = None,
     return_observations: bool = False,
     render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> dict:
     """Run a batched policy rollout once through a batch of environments.
 
@@ -145,6 +209,33 @@ def rollout(
     if render_callback is not None:
         render_callback(env)
 
+    recording_datasets: list[LeRobotDataset] | None = None
+    raw_observation = None
+    task_desc = ""
+    if recording_dir is not None and env_features is not None:
+        features = _env_features_to_dataset_features(env_features)
+        fps = env.unwrapped.metadata.get("render_fps", 30)
+        recording_datasets = []
+        multi_env = env.num_envs > 1
+        base_repo_id = recording_repo_id or "eval_recording"
+        for i in range(env.num_envs):
+            root = str(recording_dir / f"env_{i}") if multi_env else str(recording_dir)
+            repo_id = f"{base_repo_id}_env_{i}" if multi_env else base_repo_id
+            recording_datasets.append(
+                LeRobotDataset.create(
+                    repo_id=repo_id,
+                    fps=fps,
+                    features=features,
+                    root=root,
+                    use_videos=True,
+                )
+            )
+        raw_observation = deepcopy(observation)
+        try:
+            task_desc = list(env.call("task_description"))[0]
+        except (AttributeError, NotImplementedError):
+            task_desc = ""
+
     all_observations = []
     all_actions = []
     all_rewards = []
@@ -162,80 +253,112 @@ def rollout(
         leave=False,
     )
     check_env_attributes_and_types(env)
-    while not np.all(done) and step < max_steps:
-        # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
-        observation = preprocess_observation(observation)
-        if return_observations:
-            all_observations.append(deepcopy(observation))
+    try:
+        while not np.all(done) and step < max_steps:
+            # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
+            observation = preprocess_observation(observation)
+            if return_observations:
+                all_observations.append(deepcopy(observation))
 
-        # Infer "task" from sub-environments (prefer natural language description).
-        # env.call() works with both SyncVectorEnv and AsyncVectorEnv.
-        try:
-            observation["task"] = list(env.call("task_description"))
-        except (AttributeError, NotImplementedError):
+            # Infer "task" from sub-environments (prefer natural language description).
+            # env.call() works with both SyncVectorEnv and AsyncVectorEnv.
             try:
-                observation["task"] = list(env.call("task"))
+                observation["task"] = list(env.call("task_description"))
             except (AttributeError, NotImplementedError):
-                observation["task"] = [""] * env.num_envs
+                try:
+                    observation["task"] = list(env.call("task"))
+                except (AttributeError, NotImplementedError):
+                    observation["task"] = [""] * env.num_envs
 
-        # Apply environment-specific preprocessing (e.g., LiberoProcessorStep for LIBERO)
-        observation = env_preprocessor(observation)
+            # Apply environment-specific preprocessing (e.g., LiberoProcessorStep for LIBERO)
+            observation = env_preprocessor(observation)
 
-        observation = preprocessor(observation)
-        with torch.inference_mode():
-            action = policy.select_action(observation)
-        action = postprocessor(action)
+            observation = preprocessor(observation)
+            with torch.inference_mode():
+                action = policy.select_action(observation)
+            action = postprocessor(action)
 
-        action_transition = {ACTION: action}
-        action_transition = env_postprocessor(action_transition)
-        action = action_transition[ACTION]
+            action_transition = {ACTION: action}
+            action_transition = env_postprocessor(action_transition)
+            action = action_transition[ACTION]
 
-        # Convert to CPU / numpy.
-        action_numpy: np.ndarray = action.to("cpu").numpy()
-        assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)"
+            # Convert to CPU / numpy.
+            action_numpy: np.ndarray = action.to("cpu").numpy()
+            assert action_numpy.ndim == 2, "Action dimensions should be (batch, action_dim)"
 
-        # Apply the next action.
-        observation, reward, terminated, truncated, info = env.step(action_numpy)
-        if render_callback is not None:
-            render_callback(env)
+            # Apply the next action.
+            observation, reward, terminated, truncated, info = env.step(action_numpy)
+            if render_callback is not None:
+                render_callback(env)
 
-        # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't
-        # available if none of the envs finished.
-        if "final_info" in info:
-            final_info = info["final_info"]
-            if not isinstance(final_info, dict):
-                raise RuntimeError(
-                    "Unsupported `final_info` format: expected dict (Gymnasium >= 1.0). "
-                    "You're likely using an older version of gymnasium (< 1.0). Please upgrade."
+            # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't
+            # available if none of the envs finished.
+            if "final_info" in info:
+                final_info = info["final_info"]
+                if not isinstance(final_info, dict):
+                    raise RuntimeError(
+                        "Unsupported `final_info` format: expected dict (Gymnasium >= 1.0). "
+                        "You're likely using an older version of gymnasium (< 1.0). Please upgrade."
+                    )
+                successes = final_info["is_success"].tolist()
+            elif "is_success" in info:
+                is_success = info["is_success"]
+                successes = (
+                    is_success.tolist()
+                    if hasattr(is_success, "tolist")
+                    else [bool(is_success)] * env.num_envs
                 )
-            successes = final_info["is_success"].tolist()
-        elif "is_success" in info:
-            is_success = info["is_success"]
-            successes = (
-                is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
+            else:
+                successes = [False] * env.num_envs
+
+            if recording_datasets is not None and raw_observation is not None:
+                prev_done = done.copy()
+                for env_idx in range(env.num_envs):
+                    if prev_done[env_idx]:
+                        continue
+                    frame = _build_raw_frame(
+                        raw_observation,
+                        env_idx,
+                        action_numpy[env_idx],
+                        reward[env_idx],
+                        successes[env_idx],
+                        bool(terminated[env_idx] | truncated[env_idx]),
+                        task_desc,
+                        recording_datasets[env_idx].features,
+                    )
+                    recording_datasets[env_idx].add_frame(frame)
+                    if terminated[env_idx] or truncated[env_idx]:
+                        recording_datasets[env_idx].save_episode()
+                raw_observation = deepcopy(observation)
+
+            # Keep track of which environments are done so far.
+            # Mark the episode as done if we reach the maximum step limit.
+            # This ensures that the rollout always terminates cleanly at `max_steps`,
+            # and allows logging/saving (e.g., videos) to be triggered consistently.
+            done = terminated | truncated | done
+            if step + 1 == max_steps:
+                done = np.ones_like(done, dtype=bool)
+
+            all_actions.append(torch.from_numpy(action_numpy))
+            all_rewards.append(torch.from_numpy(reward))
+            all_dones.append(torch.from_numpy(done))
+            all_successes.append(torch.tensor(successes))
+
+            step += 1
+            running_success_rate = (
+                einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean()
             )
-        else:
-            successes = [False] * env.num_envs
-
-        # Keep track of which environments are done so far.
-        # Mark the episode as done if we reach the maximum step limit.
-        # This ensures that the rollout always terminates cleanly at `max_steps`,
-        # and allows logging/saving (e.g., videos) to be triggered consistently.
-        done = terminated | truncated | done
-        if step + 1 == max_steps:
-            done = np.ones_like(done, dtype=bool)
-
-        all_actions.append(torch.from_numpy(action_numpy))
-        all_rewards.append(torch.from_numpy(reward))
-        all_dones.append(torch.from_numpy(done))
-        all_successes.append(torch.tensor(successes))
-
-        step += 1
-        running_success_rate = (
-            einops.reduce(torch.stack(all_successes, dim=1), "b n -> b", "any").numpy().mean()
-        )
-        progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})
-        progbar.update()
+            progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})
+            progbar.update()
+    finally:
+        if recording_datasets is not None:
+            for ds in recording_datasets:
+                ds.finalize()
+                if recording_repo_id is not None:
+                    if ds.num_episodes > 0:
+                        ds.push_to_hub(private=recording_private)
+                    else:
+                        logging.warning("No episodes recorded for %s — skipping push to hub.", ds.repo_id)
 
     # Track the final observation.
     if return_observations:
@@ -273,6 +396,10 @@ def eval_policy(
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> dict:
     """
     Args:
@@ -361,6 +488,10 @@ def eval_policy(
             seeds=list(seeds) if seeds else None,
             return_observations=return_episode_data,
             render_callback=render_frame if max_episodes_rendered > 0 else None,
+            recording_dir=recording_dir,
+            env_features=env_features,
+            recording_repo_id=recording_repo_id,
+            recording_private=recording_private,
         )
 
         # Figure out where in each rollout sequence the first done condition was encountered (results after
@@ -563,6 +694,10 @@ def eval_main(cfg: EvalPipelineConfig):
     # Create environment-specific preprocessor and postprocessor (e.g., for LIBERO environments)
     env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env, policy_cfg=cfg.policy)
 
+    recording_dir = Path(cfg.output_dir) / "recordings" if cfg.eval.recording else None
+    max_episodes_rendered = 0 if cfg.eval.recording else 10
+    videos_dir = None if cfg.eval.recording else Path(cfg.output_dir) / "videos"
+
     with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy_all(
             envs=envs,
@@ -572,10 +707,15 @@ def eval_main(cfg: EvalPipelineConfig):
             preprocessor=preprocessor,
             postprocessor=postprocessor,
             n_episodes=cfg.eval.n_episodes,
-            max_episodes_rendered=10,
-            videos_dir=Path(cfg.output_dir) / "videos",
+            max_episodes_rendered=max_episodes_rendered,
+            videos_dir=videos_dir,
+            return_episode_data=False,
             start_seed=cfg.seed,
             max_parallel_tasks=cfg.env.max_parallel_tasks,
+            recording_dir=recording_dir,
+            env_features=cfg.env.features if cfg.eval.recording else None,
+            recording_repo_id=cfg.eval.recording_repo_id,
+            recording_private=cfg.eval.recording_private,
         )
         print("Overall Aggregated Metrics:")
         print(info["overall"])
@@ -618,6 +758,10 @@ def eval_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> TaskMetrics:
     """Evaluates one task_id of one suite using the provided vec env."""
 
@@ -635,6 +779,10 @@ def eval_one(
         videos_dir=task_videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=recording_dir,
+        env_features=env_features,
+        recording_repo_id=recording_repo_id,
+        recording_private=recording_private,
     )
 
     per_episode = task_result["per_episode"]
@@ -661,6 +809,10 @@ def run_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ):
     """
     Run eval_one for a single (task_group, task_id, env).
@@ -672,7 +824,13 @@ def run_one(
         task_videos_dir = videos_dir / f"{task_group}_{task_id}"
         task_videos_dir.mkdir(parents=True, exist_ok=True)
 
-    # Call the existing eval_one (assumed to return TaskMetrics-like dict)
+    task_recording_dir = None
+    task_repo_id = None
+    if recording_dir is not None and env_features is not None:
+        task_recording_dir = recording_dir / f"{task_group}_{task_id}"
+        if recording_repo_id is not None:
+            task_repo_id = f"{recording_repo_id}_{task_group}_{task_id}"
+
     metrics = eval_one(
         env,
         policy=policy,
@@ -685,8 +843,12 @@ def run_one(
         videos_dir=task_videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=task_recording_dir,
+        env_features=env_features,
+        recording_repo_id=task_repo_id,
+        recording_private=recording_private,
     )
-    # ensure we always provide video_paths key to simplify accumulation
+
     if max_episodes_rendered > 0:
         metrics.setdefault("video_paths", [])
     return task_group, task_id, metrics
@@ -702,6 +864,10 @@ def eval_policy_all(
     n_episodes: int,
     *,
     max_episodes_rendered: int = 0,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
@@ -761,6 +927,10 @@ def eval_policy_all(
         videos_dir=videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=recording_dir,
+        env_features=env_features,
+        recording_repo_id=recording_repo_id,
+        recording_private=recording_private,
     )
 
     if max_parallel_tasks <= 1:

uv.lock

@@ -768,34 +768,46 @@ wheels = [
 
 [[package]]
 name = "cmeel-tinyxml2"
-version = "11.0.0"
+version = "10.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cmeel" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/96/4311533fee0a364bb605b585762f04c249f47857b33548a8ea837a7eb860/cmeel_tinyxml2-11.0.0.tar.gz", hash = "sha256:85d9c7680b3369af4c6b40a0dce70bbd84aa67832755622e57eb260cd95abe40", size = 645900, upload-time = "2026-05-21T11:49:32.652Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/28/9f/030eca702c485f7a641f975f167fa93164911b3329f005fb0730ff5e793f/cmeel_tinyxml2-10.0.0.tar.gz", hash = "sha256:00252aefc1c94a55b89f25ad08ee79fda2da8d1d94703e051598ddb52a9088fe", size = 645297, upload-time = "2025-02-06T10:29:00.106Z" }
 wheels = [
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+    { name = "numpy", marker = "(platform_machine != 'arm64' and platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux')" },
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@@ -2788,6 +2800,8 @@ accelerate-dep = [
 all = [
     { name = "accelerate" },
     { name = "av" },
+    { name = "cmeel-tinyxml2" },
+    { name = "cmeel-urdfdom" },
     { name = "contourpy" },
     { name = "datasets" },
     { name = "debugpy" },
@@ -2971,6 +2985,8 @@ hardware = [
 ]
 hilserl = [
     { name = "av" },
+    { name = "cmeel-tinyxml2" },
+    { name = "cmeel-urdfdom" },
     { name = "datasets" },
     { name = "grpcio" },
     { name = "gym-hil" },
@@ -2993,6 +3009,8 @@ intelrealsense = [
     { name = "pyrealsense2-macosx", marker = "sys_platform == 'darwin'" },
 ]
 kinematics = [
+    { name = "cmeel-tinyxml2" },
+    { name = "cmeel-urdfdom" },
     { name = "placo" },
 ]
 lekiwi = [
@@ -3066,6 +3084,8 @@ pi = [
     { name = "transformers" },
 ]
 placo-dep = [
+    { name = "cmeel-tinyxml2" },
+    { name = "cmeel-urdfdom" },
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+    { name = "cmeel-urdfdom", marker = "extra == 'placo-dep'", specifier = ">=4,<5" },
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