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@@ -67,14 +67,14 @@ python src/lerobot/scripts/eval.py \
When using LIBERO through LeRobot, policies interact with the environment via **observations** and **actions**:
- **Observations**
- `observation.state` – proprioceptive features (agent state).
- `observation.images.image` – main camera view (`agentview_image`).
- `observation.images.image2` – wrist camera view (`robot0_eye_in_hand_image`).
⚠️ **Note:** LeRobot enforces the `.images.*` prefix for any visual features. Make sure your dataset metadata keys match this convention when evaluating.
- `observation.state` – proprioceptive features (agent state).
- `observation.images.image` – main camera view (`agentview_image`).
- `observation.images.image2` – wrist camera view (`robot0_eye_in_hand_image`).
⚠️ **Note:** LeRobot enforces the `.images.*` prefix for any visual features. Make sure your dataset metadata keys match this convention when evaluating.
- **Actions**
- Continuous control values in a `Box(-1, 1, shape=(7,))` space.
- Continuous control values in a `Box(-1, 1, shape=(7,))` space.
We also provide a notebook for quick testing:
Training with LIBERO
@@ -116,4 +116,4 @@ python src/lerobot/scripts/train.py \
LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation:
- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud)
- `export MUJOCO_GL=glfw` → for local runs with a display
- `export MUJOCO_GL=glfw` → for local runs with a display