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Using the roarm_m3 with LeRobot

A. Install LeRobot
B. Teleoperate
C. Record a dataset
D. Visualize a dataset
E. Replay an episode
F. Train a policy
G. Evaluate your policy
H. More Information

A. Install LeRobot

Before running the following commands, make sure you have installed LeRobot by following the installation instructions.

B. Teleoperate

Simple teleop

a. Teleop without displaying cameras

You will be able to teleoperate your robot! (it won't connect and display the cameras):

python lerobot/scripts/control_robot.py \
  --robot.type=roarm_m3 \
  --robot.cameras='{}' \
  --control.type=teleoperate

b. Teleop with displaying cameras

You will be able to display the cameras while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset.

python lerobot/scripts/control_robot.py \
  --robot.type=roarm_m3 \
  --control.type=teleoperate

C. Record a dataset

Once you're familiar with teleoperation, you can record your first dataset with roarm_m3.

If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the Hugging Face settings:

huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential

Store your Hugging Face repository name in a variable to run these commands:

HF_USER=$(huggingface-cli whoami | head -n 1)
echo $HF_USER

Record 2 episodes and upload your dataset to the hub:

python lerobot/scripts/control_robot.py \
  --robot.type=roarm_m3 \
  --control.type=record \
  --control.fps=30 \
  --control.single_task="Grasp a block and put it in the bin." \
  --control.repo_id=${HF_USER}/roarm_m3_test \
  --control.tags='["roarm_m3","tutorial"]' \
  --control.warmup_time_s=5 \
  --control.episode_time_s=30 \
  --control.reset_time_s=30 \
  --control.num_episodes=2 \
  --control.push_to_hub=true

Note: You can resume recording by adding --control.resume=true.

D. Visualize a dataset

If you uploaded your dataset to the hub with --control.push_to_hub=true, you can visualize your dataset online by copy pasting your repo id given by:

echo ${HF_USER}/roarm_m3_test

If you didn't upload with --control.push_to_hub=false, you can also visualize it locally with (a window can be opened in the browser http://ip:9090 with the visualization tool):

python lerobot/scripts/visualize_dataset_html.py \
  --repo-id ${HF_USER}/roarm_m3_test \
  --host ip \
  --local-files-only 1

E. Replay an episode

Now try to replay episode nth on your bot:

python lerobot/scripts/control_robot.py \
  --robot.type=roarm_m3 \
  --control.type=replay \
  --control.fps=30 \
  --control.repo_id=${HF_USER}/roarm_m3_test \
  --control.episode=n-1

F. Train a policy

To train a policy to control your robot, use the python lerobot/scripts/train.py script. A few arguments are required. Here is an example command:

python lerobot/scripts/train.py \
  --dataset.repo_id=${HF_USER}/roarm_m3_test \
  --policy.type=act \
  --output_dir=outputs/train/act_roarm_m3_test \
  --job_name=act_roarm_m3_test \
  --device=cuda \
  --wandb.enable=true

Let's explain it:

We provided the dataset as argument with --dataset.repo_id=${HF_USER}/roarm_m3_test.
We provided the policy with policy.type=act. This loads configurations from configuration_act.py. Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. laptop and phone) which have been saved in your dataset.
We provided device=cuda since we are training on a Nvidia GPU, but you could use device=mps to train on Apple silicon.
We provided wandb.enable=true to use Weights and Biases for visualizing training plots. This is optional but if you use it, make sure you are logged in by running wandb login.

Training should take several hours. You will find checkpoints in outputs/train/act_roarm_m3_test/checkpoints.

G. Evaluate your policy

You can use the record function from lerobot/scripts/control_robot.py but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes:

python lerobot/scripts/control_robot.py \
  --robot.type=roarm_m3 \
  --control.type=record \
  --control.fps=30 \
  --control.single_task="Grasp a block and put it in the bin." \
  --control.repo_id=${HF_USER}/eval_act_roarm_m3_test \
  --control.tags='["tutorial"]' \
  --control.warmup_time_s=5 \
  --control.episode_time_s=30 \
  --control.reset_time_s=30 \
  --control.num_episodes=10 \
  --control.push_to_hub=true \
  --control.policy.path=outputs/train/act_roarm_m3_test/checkpoints/last/pretrained_model

As you can see, it's almost the same command as previously used to record your training dataset. Two things changed:

There is an additional --control.policy.path argument which indicates the path to your policy checkpoint with (e.g. outputs/train/eval_act_roarm_m3_test/checkpoints/last/pretrained_model). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. ${HF_USER}/act_roarm_m3_test).
The name of dataset begins by eval to reflect that you are running inference (e.g. ${HF_USER}/eval_act_roarm_m3_test).

H. More Information

Follow this previous tutorial for a more in-depth tutorial on controlling real robots with LeRobot.

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