molmoact2_500eps_endeffector_trace

Model Card for molmoact2

<!-- Provide a quick summary of what the model is/does. -->

MolmoAct2 is an open robotics foundation model from the Allen Institute for AI (Ai2) that maps camera images and language instructions to robot action chunks. The LeRobot implementation supports training and evaluation of the regular MolmoAct2 model.

<!-- A short demo is worth more than any description! Record a GIF/video of the policy running on your robot, upload it to this repo, and embed it here: <p align="center"> <img src="https://huggingface.co/<hf_user>/<policy_repo_id>/resolve/main/demo.gif" width="60%"/> </p> -->

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot molmoact2 guide, or browse the full documentation.

---

Model Details

• License: apache-2.0
• Robot type: so_follower
• Cameras: wrist, right, left, middle

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Outputs

Training Dataset

• Repository: mattpidden/500eps-endeffector-trace-dataset
• Episodes: 498
• Frames: 137695
• Frame rate: 30 FPS
• Task(s): "Drag the cloth across the table.", "Drag the cup across the table.", "Knock over the box onto its side.", "Knock over the cup onto its side..", "Knock over the soda can onto its side.", "Open the cup by lifting the lid.", "Open the spice jar by lifting the lid.", "Pick up the apple and place it in the bowl.", "Pick up the block and place it next to the pencil basket.", "Pick up the box and place it in the bowl.", "Pick up the juggling ball and place it in the bowl.", "Pick up the marker and insert it into the pencil basket.", "Pick up the straw from the basket and insert it into the cup.", "Pick up the straw from the basket and insert it into the jar.", "Push the apple next to the block.", "Push the cardboard box next to the block.", "Push the cup next to the other cup.", "Push the juggling ball next to the bowl.", "Reorient the block upright.", "Reorient the box to stand vertically on its end.", "Reorient the cup upright.", "Reorient the spice jar upright.", "Stack the block on top of the apple.", "Stack the blocks on top of another block.", "Stack the white cup into the brown cup."

<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=mattpidden/500eps-endeffector-trace-dataset"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/> </a>

Training Configuration

---

How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

• Install LeRobot — set up the lerobot package.
• Hardware setup — assemble, wire, and calibrate your robot and cameras.
• Record data & train a policy — the end-to-end imitation-learning walkthrough.
• CLI cheat-sheet — quick reference for the lerobot-* commands.

The short version to run and train this policy:

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=so_follower \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=justintiensmith/molmoact2_500eps_endeffector_trace \
  --task="Drag the cloth across the table." \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.

Train your own policy

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=molmoact2 \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.

---

Evaluation

<!-- Report real-robot results here: run the policy several times per task and count the successes. Delete the "No evaluation results" line and fill in this table instead: | Task | Trials | Successes | Success rate | | ---- | ------ | --------- | ------------ | | pick the lego brick | 10 | 8 | 80% | Also worth noting: anything that affects difficulty (new object positions, lighting, distractors, a different robot of the same type, ...). -->

No evaluation results have been provided for this policy yet.

---

Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}