docs(groot): add LIBERO training command example

docs/source/groot.mdx

@@ -115,6 +115,41 @@ uv run lerobot-train \
 
 GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.
 
+### Train on LIBERO
+
+Example training command for a LIBERO suite (here `libero_spatial`):
+
+```bash
+lerobot-train \
+  --dataset.repo_id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \
+  --dataset.root=/datasets/libero_spatial \
+  --dataset.revision=main \
+  --dataset.video_backend=pyav \
+  --policy.type=groot \
+  --policy.base_model_path=$BASE_MODEL \
+  --policy.embodiment_tag=libero_sim \
+  --policy.push_to_hub=false \
+  --policy.max_steps=20000 \
+  --batch_size=320 \
+  --steps=20000 \
+  --save_freq=2000 \
+  --env_eval_freq=0 \
+  --eval_steps=0 \
+  --log_freq=10 \
+  --wandb.enable=true \
+  --wandb.project=lerobot \
+  --wandb.mode=online \
+  --wandb.disable_artifact=true \
+  --num_workers=4 \
+  --prefetch_factor=2 \
+  --persistent_workers=true \
+  --output_dir=$OUTPUT_DIR \
+  --job_name=$JOB_NAME \
+  --dataset.image_transforms.enable=true \
+  --dataset.image_transforms.max_num_transforms=4 \
+  --dataset.image_transforms.tfs='{"brightness":{"weight":1.0,"type":"ColorJitter","kwargs":{"brightness":[0.7,1.3]}},"contrast":{"weight":1.0,"type":"ColorJitter","kwargs":{"contrast":[0.6,1.4]}},"saturation":{"weight":1.0,"type":"ColorJitter","kwargs":{"saturation":[0.5,1.5]}},"hue":{"weight":1.0,"type":"ColorJitter","kwargs":{"hue":[-0.08,0.08]}}}'
+```
+
 ### GR00T N1.7 LIBERO Checkpoints
 
 NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](https://huggingface.co/nvidia/GR00T-N1.7-LIBERO), with one subdirectory per LIBERO suite: