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docs/source/libero.mdx

@@ -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
@@ -92,16 +92,16 @@ observation.images.image2 → wrist camera (robot0_eye_in_hand_image)
 
 Example training command
 python src/lerobot/scripts/train.py \
-  --policy.type=smolvla \
-  --dataset.repo_id=jadechoghari/smol-libero3 \
-  --env.type=libero \
-  --env.task=libero_10,libero_spatial \
-  --output_dir=./outputs/ \
-  --steps=100000 \
-  --batch_size=4 \
-  --env.multitask_eval=True \
-  --eval.batch_size=1 \
-  --eval.n_episodes=1
+ --policy.type=smolvla \
+ --dataset.repo_id=jadechoghari/smol-libero3 \
+ --env.type=libero \
+ --env.task=libero_10,libero_spatial \
+ --output_dir=./outputs/ \
+ --steps=100000 \
+ --batch_size=4 \
+ --env.multitask_eval=True \
+ --eval.batch_size=1 \
+ --eval.n_episodes=1
 
 Note on rendering
 
@@ -109,4 +109,4 @@ LeRobot uses MuJoCo for simulation. You need to set the rendering backend before
 
 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