Fix gym-hil integration with the new LeRobot pipeline. (#2482)

* Add GymHILAdapterProcessorStep for gym-hil environment integration

* Fix action features in control loop for None teleop device with gym-hil

* Finalize dataset before pushing to hub for visualization on the hub

* Fix neutral action for gripper

* fix pre-commit
This commit is contained in:
Khalil
2026-02-19 14:35:02 +01:00
committed by GitHub
parent 5f15232271
commit 2dd366436e
4 changed files with 54 additions and 2 deletions

View File

@@ -312,6 +312,37 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
return features
@ProcessorStepRegistry.register("gym_hil_adapter_processor")
class GymHILAdapterProcessorStep(ProcessorStep):
"""
Adapts the output of the `gym-hil` environment to the format expected by `lerobot` processors.
This step normalizes the `transition` object by:
1. Copying `teleop_action` from `info` to `complementary_data`.
2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
"""
def __call__(self, transition: EnvTransition) -> EnvTransition:
info = transition.get(TransitionKey.INFO, {})
complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
if TELEOP_ACTION_KEY in info:
complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
if "is_intervention" in info:
info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
transition[TransitionKey.INFO] = info
transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
return transition
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
return features
@dataclass
@ProcessorStepRegistry.register("gripper_penalty_processor")
class GripperPenaltyProcessorStep(ProcessorStep):