fix(envs): use RoboCasa task horizons (#4037)

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

docs/source/robocasa.mdx

@@ -82,6 +82,8 @@ By default the env samples objects only from the `lightwheel` registry (what `--
 
 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.
 
+By default, each task uses the rollout horizon registered by RoboCasa. Set `--env.episode_length=<steps>` to apply the same explicit horizon to every selected task.
+
 ### Single-task evaluation (recommended for quick iteration)
 
 ```bash

src/lerobot/envs/configs.py

@@ -507,7 +507,7 @@ class MetaworldEnv(EnvConfig):
 class RoboCasaEnv(EnvConfig):
     task: str = "CloseFridge"
     fps: int = 20
-    episode_length: int = 1000
+    episode_length: int | None = None
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "robot0_agentview_left,robot0_eye_in_hand,robot0_agentview_right"

src/lerobot/envs/robocasa.py

@@ -98,6 +98,19 @@ def _resolve_tasks(task: str) -> tuple[list[str], str | None]:
     return names, None
 
 
+def _get_task_horizon(task: str) -> int:
+    """Return the rollout horizon registered by RoboCasa for a task."""
+    from robocasa.utils.dataset_registry_utils import get_task_horizon
+
+    try:
+        return int(get_task_horizon(task))
+    except ValueError as exc:
+        raise ValueError(
+            f"No RoboCasa horizon is registered for task '{task}'. "
+            "Set `--env.episode_length=<steps>` explicitly."
+        ) from exc
+
+
 def convert_action(flat_action: np.ndarray) -> dict[str, Any]:
     """Split a flat (12,) action vector into a RoboCasa action dict.
 
@@ -154,7 +167,7 @@ class RoboCasaEnv(gym.Env):
 
         self.camera_name = parse_camera_names(camera_name)
 
-        self._max_episode_steps = episode_length if episode_length is not None else 1000
+        self._max_episode_steps = episode_length if episode_length is not None else _get_task_horizon(task)
 
         # Deferred — created on first reset() inside the worker subprocess
         # to avoid inheriting stale GPU/EGL contexts across fork().

tests/envs/test_robocasa.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from unittest.mock import Mock, call
+
+import pytest
+
+from lerobot.envs import robocasa
+from lerobot.envs.configs import RoboCasaEnv as RoboCasaEnvConfig
+
+
+def _instantiate_envs(
+    factories: Sequence[Callable[[], robocasa.RoboCasaEnv]],
+) -> list[robocasa.RoboCasaEnv]:
+    return [factory() for factory in factories]
+
+
+def test_robocasa_config_uses_registered_horizon_by_default() -> None:
+    assert RoboCasaEnvConfig().episode_length is None
+
+
+def test_multi_task_envs_use_registered_horizons(monkeypatch: pytest.MonkeyPatch) -> None:
+    horizons = {"CloseFridge": 900, "SearingMeat": 4350}
+    get_task_horizon = Mock(side_effect=horizons.__getitem__)
+    monkeypatch.setattr(robocasa, "_get_task_horizon", get_task_horizon)
+
+    envs = robocasa.create_robocasa_envs(
+        task="CloseFridge,SearingMeat",
+        n_envs=1,
+        env_cls=_instantiate_envs,
+    )
+
+    assert envs["CloseFridge"][0][0]._max_episode_steps == 900
+    assert envs["SearingMeat"][0][0]._max_episode_steps == 4350
+    assert get_task_horizon.call_args_list == [call("CloseFridge"), call("SearingMeat")]
+
+
+def test_explicit_episode_length_overrides_registered_horizons(monkeypatch: pytest.MonkeyPatch) -> None:
+    get_task_horizon = Mock()
+    monkeypatch.setattr(robocasa, "_get_task_horizon", get_task_horizon)
+
+    envs = robocasa.create_robocasa_envs(
+        task="CloseFridge,SearingMeat",
+        n_envs=1,
+        env_cls=_instantiate_envs,
+        episode_length=1234,
+    )
+
+    assert envs["CloseFridge"][0][0]._max_episode_steps == 1234
+    assert envs["SearingMeat"][0][0]._max_episode_steps == 1234
+    get_task_horizon.assert_not_called()