fix: integrate PR #3375 review feedback

- envs(robocasa): hoist the duplicated `_parse_camera_names` helper
  out of `libero.py` and `robocasa.py` into `envs/utils.py` as the
  public `parse_camera_names`; call sites updated.
- envs(robocasa): give each factory a distinct `episode_index`
  (`0..n_envs-1`) and derive a per-worker seed series in `reset()`
  so n_envs workers don't all roll the same scene under a shared
  outer seed.
- envs(robocasa): drop the unused `**kwargs` on `_make_env`; declare
  `visualization_height` / `visualization_width` on both the wrapper
  and the `RoboCasaEnv` config + propagate via `gym_kwargs`.
- envs(robocasa): emit `info["final_info"]` on termination (matching
  MetaWorld) so downstream vector-env auto-reset keeps the terminal
  task/success flags.
- docs(robocasa): add `--rename_map` (robot0_agentview_left/
  eye_in_hand/agentview_right → camera1/2/3) plus CI-parity flags to
  all three eval snippets.
- docker(robocasa): pin robocasa + robosuite git SHAs and the pip
  dep versions (pygame, Pillow, opencv-python, pyyaml, pynput, tqdm,
  termcolor, imageio, h5py, lxml, hidapi, gymnasium) for
  reproducible benchmark images.
- ci(robocasa): update the workflow comment — there is no
  `lerobot[robocasa]` extra; robocasa/robosuite are installed
  manually because upstream's `lerobot==0.3.3` pin shadows ours.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.github/workflows/benchmark_tests.yml

@@ -312,7 +312,9 @@ jobs:
           if-no-files-found: warn
 
   # ── ROBOCASA365 ──────────────────────────────────────────────────────────
-  # Isolated image: lerobot[robocasa] only (robocasa, robosuite, mujoco chain)
+  # Isolated image: robocasa + robosuite installed manually as editable
+  # clones (no `lerobot[robocasa]` extra — robocasa's setup.py pins
+  # `lerobot==0.3.3`, which would shadow this repo's lerobot).
   robocasa-integration-test:
     name: RoboCasa365 — build image + 1-episode eval
     runs-on:

docker/Dockerfile.benchmark.robocasa

@@ -28,14 +28,22 @@ FROM huggingface/lerobot-gpu:latest
 # `--no-deps` on robocasa is deliberate: its setup.py pins `lerobot==0.3.3`
 # in install_requires, which would shadow the editable lerobot baked into
 # this image. We install robocasa's actual runtime deps explicitly instead.
-RUN git clone --depth 1 https://github.com/robocasa/robocasa.git ~/robocasa && \
+# Pinned SHAs for reproducible benchmark runs. Bump when you need an
-    git clone --depth 1 https://github.com/ARISE-Initiative/robosuite.git ~/robosuite && \
+# upstream fix; don't rely on `main`/`master` drift.
+ARG ROBOCASA_SHA=56e355ccc64389dfc1b8a61a33b9127b975ba681
+ARG ROBOSUITE_SHA=aaa8b9b214ce8e77e82926d677b4d61d55e577ab
+RUN git clone https://github.com/robocasa/robocasa.git ~/robocasa && \
+    git -C ~/robocasa checkout ${ROBOCASA_SHA} && \
+    git clone https://github.com/ARISE-Initiative/robosuite.git ~/robosuite && \
+    git -C ~/robosuite checkout ${ROBOSUITE_SHA} && \
     uv pip install --no-cache -e ~/robocasa --no-deps && \
     uv pip install --no-cache -e ~/robosuite && \
     uv pip install --no-cache \
       "numpy==2.2.5" "numba==0.61.2" "scipy==1.15.3" "mujoco==3.3.1" \
-      pygame Pillow opencv-python pyyaml pynput tqdm termcolor \
+      "pygame==2.6.1" "Pillow==12.2.0" "opencv-python==4.13.0.92" \
-      imageio h5py lxml hidapi "tianshou==0.4.10" gymnasium
+      "pyyaml==6.0.3" "pynput==1.8.1" "tqdm==4.67.3" "termcolor==3.3.0" \
+      "imageio==2.37.3" "h5py==3.16.0" "lxml==6.0.4" "hidapi==0.14.0.post4" \
+      "tianshou==0.4.10" "gymnasium==1.2.3"
 
 # Set up robocasa macros and download kitchen assets. We need:
 #   - tex              : base environment textures

docs/source/robocasa.mdx

@@ -74,6 +74,8 @@ By default the env samples objects only from the `lightwheel` registry (what `--
 
 ## Evaluation
 
+All eval snippets below mirror the CI command (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps RoboCasa's native camera keys (`robot0_agentview_left` / `robot0_eye_in_hand` / `robot0_agentview_right`) onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_robocasa` policy was trained on.
+
 ### Single-task evaluation (recommended for quick iteration)
 
 ```bash
@@ -82,7 +84,10 @@ lerobot-eval \
   --env.type=robocasa \
   --env.task=CloseFridge \
   --eval.batch_size=1 \
-  --eval.n_episodes=20
+  --eval.n_episodes=20 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
 ```
 
 ### Multi-task evaluation
@@ -95,7 +100,10 @@ lerobot-eval \
   --env.type=robocasa \
   --env.task=CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove \
   --eval.batch_size=1 \
-  --eval.n_episodes=20
+  --eval.n_episodes=20 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
 ```
 
 ### Benchmark-group evaluation
@@ -108,7 +116,10 @@ lerobot-eval \
   --env.type=robocasa \
   --env.task=atomic_seen \
   --eval.batch_size=1 \
-  --eval.n_episodes=20
+  --eval.n_episodes=20 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
 ```
 
 ### Recommended evaluation episodes

src/lerobot/envs/configs.py

@@ -507,6 +507,8 @@ class RoboCasaEnv(EnvConfig):
     camera_name: str = "robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right"
     observation_height: int = 256
     observation_width: int = 256
+    visualization_height: int = 512
+    visualization_width: int = 512
     split: str | None = None
     # Object-mesh registries to sample from. Upstream default is
     # ("objaverse", "lightwheel"), but objaverse is ~30GB and the CI image
@@ -545,6 +547,8 @@ class RoboCasaEnv(EnvConfig):
             "render_mode": self.render_mode,
             "observation_height": self.observation_height,
             "observation_width": self.observation_width,
+            "visualization_height": self.visualization_height,
+            "visualization_width": self.visualization_width,
         }
         if self.split is not None:
             kwargs["split"] = self.split

src/lerobot/envs/libero.py

@@ -31,20 +31,7 @@ from libero.libero.envs import OffScreenRenderEnv
 
 from lerobot.types import RobotObservation
 
-from .utils import _LazyAsyncVectorEnv
+from .utils import _LazyAsyncVectorEnv, parse_camera_names
-
-
-def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
-    """Normalize camera_name into a non-empty list of strings."""
-    if isinstance(camera_name, str):
-        cams = [c.strip() for c in camera_name.split(",") if c.strip()]
-    elif isinstance(camera_name, (list | tuple)):
-        cams = [str(c).strip() for c in camera_name if str(c).strip()]
-    else:
-        raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")
-    if not cams:
-        raise ValueError("camera_name resolved to an empty list.")
-    return cams
 
 
 def _get_suite(name: str) -> benchmark.Benchmark:
@@ -128,7 +115,7 @@ class LiberoEnv(gym.Env):
         self.visualization_width = visualization_width
         self.visualization_height = visualization_height
         self.init_states = init_states
-        self.camera_name = _parse_camera_names(
+        self.camera_name = parse_camera_names(
             camera_name
         )  # agentview_image (main) or robot0_eye_in_hand_image (wrist)
 
@@ -437,7 +424,7 @@ def create_libero_envs(
     gym_kwargs = dict(gym_kwargs or {})
     task_ids_filter = gym_kwargs.pop("task_ids", None)  # optional: limit to specific tasks
 
-    camera_names = _parse_camera_names(camera_name)
+    camera_names = parse_camera_names(camera_name)
     suite_names = [s.strip() for s in str(task).split(",") if s.strip()]
     if not suite_names:
         raise ValueError("`task` must contain at least one LIBERO suite name.")

src/lerobot/envs/robocasa.py

@@ -26,7 +26,7 @@ from gymnasium import spaces
 
 from lerobot.types import RobotObservation
 
-from .utils import _LazyAsyncVectorEnv
+from .utils import _LazyAsyncVectorEnv, parse_camera_names
 
 # Dimensions for the flat action/state vectors used by the LeRobot wrapper.
 # These correspond to the PandaOmron robot in RoboCasa365.
@@ -69,19 +69,6 @@ _TASK_GROUP_SPLITS = {
 }
 
 
-def _parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
-    """Normalize camera_name into a non-empty list of strings."""
-    if isinstance(camera_name, str):
-        cams = [c.strip() for c in camera_name.split(",") if c.strip()]
-    elif isinstance(camera_name, (list | tuple)):
-        cams = [str(c).strip() for c in camera_name if str(c).strip()]
-    else:
-        raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")
-    if not cams:
-        raise ValueError("camera_name resolved to an empty list.")
-    return cams
-
-
 def _resolve_tasks(task: str) -> tuple[list[str], str | None]:
     """Resolve a `--env.task` value to (task_names, split_override).
 
@@ -140,9 +127,12 @@ class RoboCasaEnv(gym.Env):
         render_mode: str = "rgb_array",
         observation_width: int = 256,
         observation_height: int = 256,
+        visualization_width: int = 512,
+        visualization_height: int = 512,
         split: str | None = None,
         episode_length: int | None = None,
         obj_registries: Sequence[str] = DEFAULT_OBJ_REGISTRIES,
+        episode_index: int = 0,
     ):
         super().__init__()
         self.task = task
@@ -150,10 +140,16 @@ class RoboCasaEnv(gym.Env):
         self.render_mode = render_mode
         self.observation_width = observation_width
         self.observation_height = observation_height
+        self.visualization_width = visualization_width
+        self.visualization_height = visualization_height
         self.split = split
         self.obj_registries = tuple(obj_registries)
+        # Per-worker index (0..n_envs-1) used to spread the user-provided
+        # seed across factories so each sub-env explores a distinct layout
+        # even when the same seed is passed to `reset()`.
+        self.episode_index = int(episode_index)
 
-        self.camera_name = _parse_camera_names(camera_name)
+        self.camera_name = parse_camera_names(camera_name)
 
         self._max_episode_steps = episode_length if episode_length is not None else 1000
 
@@ -253,7 +249,12 @@ class RoboCasaEnv(gym.Env):
         self._ensure_env()
         assert self._env is not None
         super().reset(seed=seed)
-        raw_obs, info = self._env.reset(seed=seed)
+        # Spread the user seed across workers. With n_envs factories each
+        # carrying a distinct `episode_index`, the same outer seed produces
+        # a different layout/trajectory per worker instead of all workers
+        # rolling the same scene.
+        worker_seed = seed + self.episode_index if seed is not None else None
+        raw_obs, info = self._env.reset(seed=worker_seed)
 
         ep_meta = self._env.env.get_ep_meta()
         self.task_description = ep_meta.get("lang", self.task)
@@ -280,6 +281,11 @@ class RoboCasaEnv(gym.Env):
 
         observation = self._format_raw_obs(raw_obs)
         if terminated:
+            info["final_info"] = {
+                "task": self.task,
+                "done": bool(done),
+                "is_success": bool(is_success),
+            }
             self.reset()
 
         return observation, reward, terminated, truncated, info
@@ -298,13 +304,20 @@ def _make_env_fns(
     render_mode: str,
     observation_width: int,
     observation_height: int,
+    visualization_width: int,
+    visualization_height: int,
     split: str | None,
     episode_length: int | None,
     obj_registries: Sequence[str],
 ) -> list[Callable[[], RoboCasaEnv]]:
-    """Build n_envs factory callables for a single task."""
+    """Build n_envs factory callables for a single task.
 
-    def _make_env(**kwargs) -> RoboCasaEnv:
+    Each factory carries a distinct ``episode_index`` (``0..n_envs-1``) so
+    ``RoboCasaEnv.reset()`` can derive a per-worker seed series from the
+    user-provided seed.
+    """
+
+    def _make_env(episode_index: int) -> RoboCasaEnv:
         return RoboCasaEnv(
             task=task,
             camera_name=camera_names,
@@ -312,13 +325,15 @@ def _make_env_fns(
             render_mode=render_mode,
             observation_width=observation_width,
             observation_height=observation_height,
+            visualization_width=visualization_width,
+            visualization_height=visualization_height,
             split=split,
             episode_length=episode_length,
             obj_registries=obj_registries,
-            **kwargs,
+            episode_index=episode_index,
         )
 
-    return [partial(_make_env) for _ in range(n_envs)]
+    return [partial(_make_env, i) for i in range(n_envs)]
 
 
 def create_robocasa_envs(
@@ -353,9 +368,11 @@ def create_robocasa_envs(
     render_mode = gym_kwargs.pop("render_mode", "rgb_array")
     observation_width = gym_kwargs.pop("observation_width", 256)
     observation_height = gym_kwargs.pop("observation_height", 256)
+    visualization_width = gym_kwargs.pop("visualization_width", 512)
+    visualization_height = gym_kwargs.pop("visualization_height", 512)
     split = gym_kwargs.pop("split", None)
 
-    camera_names = _parse_camera_names(camera_name)
+    camera_names = parse_camera_names(camera_name)
     task_names, group_split = _resolve_tasks(str(task))
     if group_split is not None and split is None:
         split = group_split
@@ -377,6 +394,8 @@ def create_robocasa_envs(
             render_mode=render_mode,
             observation_width=observation_width,
             observation_height=observation_height,
+            visualization_width=visualization_width,
+            visualization_height=visualization_height,
             split=split,
             episode_length=episode_length,
             obj_registries=obj_registries,

src/lerobot/envs/utils.py

@@ -34,6 +34,25 @@ from lerobot.utils.utils import get_channel_first_image_shape
 from .configs import EnvConfig
 
 
+def parse_camera_names(camera_name: str | Sequence[str]) -> list[str]:
+    """Normalize ``camera_name`` into a non-empty list of strings.
+
+    Accepts a comma-separated string (``"cam_a,cam_b"``) or a sequence of
+    strings (tuples/lists). Whitespace is stripped; empty entries are
+    dropped. Raises ``TypeError`` for unsupported input types and
+    ``ValueError`` when the normalized list is empty.
+    """
+    if isinstance(camera_name, str):
+        cams = [c.strip() for c in camera_name.split(",") if c.strip()]
+    elif isinstance(camera_name, (list | tuple)):
+        cams = [str(c).strip() for c in camera_name if str(c).strip()]
+    else:
+        raise TypeError(f"camera_name must be str or sequence[str], got {type(camera_name).__name__}")
+    if not cams:
+        raise ValueError("camera_name resolved to an empty list.")
+    return cams
+
+
 def _convert_nested_dict(d):
     result = {}
     for k, v in d.items():