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docs/source/libero.mdx

@@ -15,7 +15,7 @@ LIBERO includes **five task suites**:
 Together, these suites cover **130 tasks**, ranging from simple object manipulations to complex multi-step scenarios. LIBERO is meant to grow over time, and to serve as a shared benchmark where the community can test and improve lifelong learning algorithms.
 
 ![An overview of the LIBERO benchmark](https://libero-project.github.io/assets/img/libero/fig1.png)
-*Figure 1: An overview of the LIBERO benchmark.*
+_Figure 1: An overview of the LIBERO benchmark._
 
 ## Evaluating with LIBERO
 
@@ -27,6 +27,7 @@ To install LIBERO, first follow the [LeRobot Installation Guide](https://hugging
 Once LeRobot is installed, there are two options:
 
 1. **Install via pip** (recommended):
+
    ```bash
    pip install "lerobot[libero,smolvla]"
    ```
@@ -85,6 +86,7 @@ When using LIBERO through LeRobot, policies interact with the environment via **
   - `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.
+
   ## Input Features and Metadata Alignment
 
   To train or evaluate a policy, you use `make_policy`, which builds a feature-naming dictionary for the observations the policy expects.
@@ -101,27 +103,28 @@ When using LIBERO through LeRobot, policies interact with the environment via **
 
   Such mismatches will cause `KeyError`s. This may be due to assumptions in `make_policy` or missing error handling.
 
-  ---
+  ***
 
   ### How to Check Expected Features
-
   - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).
   - Or add a breakpoint in `train.py` and inspect:
-    ```python
+
+    ````python
     print(policy.config.input_features)
-  To ensure you can just check what your policy expects as `input_features`:
+    To ensure you can just check what your policy expects as `input_features`:
 
     - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).
     - Or add a breakpoint in `train.py` and inspect:
       ```python
       print(policy.config.input_features)
-  Fixing KeyErrors (Preprocessing Example)
+    Fixing KeyErrors (Preprocessing Example)
+    ````
 
 ## Fixing KeyErrors (Preprocessing Example)
 
 If your dataset columns do not follow the expected naming, you can rename them in-place before training:
 
-```python
+````python
 import pyarrow.parquet as pq
 import shutil
 
@@ -187,7 +190,7 @@ python src/lerobot/scripts/train.py \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
   --eval_freq=1000 \
-```
+````
 
 ---
 
@@ -213,11 +216,13 @@ This happens because Colab’s rendering contexts are **not thread-safe**, and `
 Parallel evaluation is not supported in Colab. To avoid these issues, run sequentially or disable multitask evaluation:
 
 Run sequentially:
+
 ```bash
 --env.max_parallel_tasks=1
 ```
 
 Or disable multitask evaluation:
+
 ```bash
 --env.multitask_eval=False
 ```