Model Inference with LeRobot CLI

Once your model is trained, you can deploy it on the AI Worker for inference.

Model Deployment and Inference

1. Transfer Model to Robot PC

INFO

If you trained your model directly on NVIDIA Jetson AGX Orin, you can skip this step.

Change ownership of the model directory. This step must be performed on the robot PC, not inside the Docker container:

sudo chown -R robotis ./

Move your model folder from your local PC to the model directory on the Robot PC using scp:

scp -r <your model folder's directory> robotis@<your robot's serial number>.local:~/ai_worker/docker/lerobot/outputs/train

2. Open a Terminal and Enter the Docker container

cd ai_worker && ./docker/container.sh enter

3. Launch the ROS 2 Follower Node

ffw_bg2_follower_ai

4. Run Inference

a. Open a New Terminal and Run Docker Container

Open a terminal on the Jetson device and enter the Docker container:

cd ai_worker && ./docker/container.sh enter

b. Navigate to the LeRobot Directory

cd /root/ros2_ws/src/physical_ai_tools/lerobot

c. Run the Following Command for Evaluation

python lerobot/scripts/control_robot.py \
  --robot.type=ffw \
  --control.type=record \
  --control.single_task="pick and place objects" \
  --control.fps=15 \
  --control.repo_id=${HF_USER}/eval_ffw_test \
  --control.tags='["tutorial"]' \
  --control.episode_time_s=20 \
  --control.reset_time_s=10 \
  --control.num_episodes=10 \
  --control.push_to_hub=true \
  --control.policy.path=outputs/train/act_ffw_test/checkpoints/last/pretrained_model \
  --control.play_sounds=false

👉 Key Inference Parameters

Parameter	Description
--control.type=record	Records the policy performance for later evaluation
--control.policy.path	Path to your trained model checkpoint
--control.episode_time_s	Duration of each inference episode (in seconds)
--control.repo_id	Hugging Face repo where evaluation results will be saved

Visualizing Inference Results

After running inference, you can visualize the results using the same visualization tool used for datasets:

python lerobot/scripts/visualize_dataset_html.py \
  --host 0.0.0.0 \
  --port 9091 \
  --repo-id ${HF_USER}/eval_ffw_test

Then open http://127.0.0.1:9091 in your browser to see how your model performed.

TIP

If you have another device connected to the same network as the host machine, open http://{robot type}-{serial number}.local:9091 in your browser to see how your model performed.

For example, http://ffw-SNPR48A0000.local:9091.

Troubleshooting

• Out of memory errors: Try reducing the batch size with --train.batch_size=16 or lower
• Low performance: Consider collecting more diverse training data or increasing training duration
• Robot not responding: Ensure the follower node is running and communication is established
• Training divergence: Check your dataset quality and try decreasing the learning rate