Robotics
LeRobot
Safetensors
molmoact2
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Upload model card and train config

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  1. README.md +164 -0
  2. train_config.json +350 -0
README.md ADDED
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+ ---
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+ datasets: VibeCuisine/cucumber-peel-DAgger-iter2-v2-trim
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+ library_name: lerobot
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+ license: apache-2.0
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+ model_name: molmoact2
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+ pipeline_tag: robotics
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+ tags:
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+ - robotics
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+ - molmoact2
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+ - lerobot
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+ ---
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+
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+ # Model Card for molmoact2
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+ [MolmoAct2](https://allenai.org/blog/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.
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+
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+
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+
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+
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+ <!-- A short demo is worth more than any description! Record a GIF/video of the policy
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+ running on your robot, upload it to this repo, and embed it here:
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+ <p align="center">
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+ <img src="https://huggingface.co/<hf_user>/<policy_repo_id>/resolve/main/demo.gif" width="60%"/>
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+ </p>
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+ -->
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+
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+ This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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+
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+ Learn how to train and run it in the [LeRobot molmoact2 guide](https://huggingface.co/docs/lerobot/main/en/molmoact2), or browse the [full documentation](https://huggingface.co/docs/lerobot/index).
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+
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+
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+ ---
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+
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+ ## Model Details
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+
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+ - **License:** apache-2.0
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+ - **Robot type:** `vibeboard_follower_tilt`
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+ - **Cameras:** `top`, `wrist`, `base`
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+
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+
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+ ## Inputs & Outputs
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+
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+ The policy consumes these observation features and produces these action features.
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+
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+ **Inputs**
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+
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+ | Feature | Type | Shape |
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+ | --- | --- | --- |
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+ | `observation.state` | STATE | `(7,)` |
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+ | `observation.images.top` | VISUAL | `(3, 480, 640)` |
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+ | `observation.images.wrist` | VISUAL | `(3, 480, 640)` |
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+ | `observation.images.base` | VISUAL | `(3, 480, 640)` |
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+
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+ **Outputs**
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+
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+ | Feature | Type | Shape |
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+ | --- | --- | --- |
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+ | `action` | ACTION | `(7,)` |
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+
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+
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+ ## Training Dataset
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+
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+ - **Repository:** [VibeCuisine/cucumber-peel-DAgger-iter2-v2-trim](https://huggingface.co/datasets/VibeCuisine/cucumber-peel-DAgger-iter2-v2-trim)
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+ - **Episodes:** 188
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+ - **Frames:** 58994
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+ - **Frame rate:** 20 FPS
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+ - **Task(s):** "peel the skin of the cucumber with several strokes, starting close to the blue gripper and moving vertically along the cucumber toward the robot origin"
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+
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+ <a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=VibeCuisine/cucumber-peel-DAgger-iter2-v2-trim">
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+ <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
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+ <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
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+ </a>
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+
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+
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+ ## Training Configuration
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+
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+ | Setting | Value |
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+ | --- | --- |
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+ | Training steps | 10 |
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+ | Batch size | 8 |
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+ | Optimizer | molmoact2_adamw |
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+ | Learning rate | 1e-05 |
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+ | Seed | 42 |
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+ | LeRobot version | 0.6.2 |
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+
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+ ---
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+
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+ ## How to Get Started with the Model
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+
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+ New to LeRobot? These guides cover the full workflow:
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+
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+ - **[Install LeRobot](https://huggingface.co/docs/lerobot/main/en/installation)** — set up the `lerobot` package.
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+ - **[Hardware setup](https://huggingface.co/docs/lerobot/main/en/hardware_guide)** — assemble, wire, and calibrate your robot and cameras.
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+ - **[Record data & train a policy](https://huggingface.co/docs/lerobot/en/il_robots)** — the end-to-end imitation-learning walkthrough.
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+ - **[CLI cheat-sheet](https://huggingface.co/docs/lerobot/main/en/cheat-sheet)** — quick reference for the `lerobot-*` commands.
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+
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+ The short version to run and train this policy:
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+
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+ ### Run the policy on your robot
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+
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+ ```bash
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+ lerobot-rollout \
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+ --strategy.type=base \
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+ --robot.type=vibeboard_follower_tilt \
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+ --robot.port=<your_robot_port> \
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+ --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}}" \
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+ --policy.path=VibeCuisine/vds-smoke-20260909-gcp-molmoact2-4g-spot \
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+ --task="peel the skin of the cucumber with several strokes, starting close to the blue gripper and moving vertically along the cucumber toward the robot origin" \
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+ --duration=60
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+ ```
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+
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+ 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.
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+
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+ 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](https://huggingface.co/docs/lerobot/main/en/inference).
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+
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+ ### Train your own policy
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+
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+ ```bash
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+ lerobot-train \
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+ --dataset.repo_id=${HF_USER}/<dataset> \
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+ --policy.type=molmoact2 \
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+ --output_dir=outputs/train/<policy_repo_id> \
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+ --job_name=lerobot_training \
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+ --policy.device=cuda \
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+ --policy.repo_id=${HF_USER}/<policy_repo_id> \
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+ --wandb.enable=true
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+ ```
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+
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+ _Writes checkpoints to `outputs/train/<policy_repo_id>/checkpoints/`._
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+
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+ ---
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+
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+ ## Evaluation
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+
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+ <!-- Report real-robot results here: run the policy several times per task and count the
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+ successes. Delete the "No evaluation results" line and fill in this table instead:
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+
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+ | Task | Trials | Successes | Success rate |
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+ | ---- | ------ | --------- | ------------ |
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+ | pick the lego brick | 10 | 8 | 80% |
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+
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+ Also worth noting: anything that affects difficulty (new object positions, lighting,
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+ distractors, a different robot of the same type, ...).
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+ -->
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+
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+ _No evaluation results have been provided for this policy yet._
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use this policy, please cite the method linked in the description above, along with LeRobot:
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+
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+ ```bibtex
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+ @misc{cadene2024lerobot,
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+ 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},
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+ title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
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+ howpublished = "\url{https://github.com/huggingface/lerobot}",
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+ year = {2024}
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+ }
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+ ```
train_config.json ADDED
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+ {
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+ "dataset": {
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+ "repo_id": "VibeCuisine/cucumber-peel-DAgger-iter2-v2-trim",
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+ "enable": true,
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+ "random_order": false,
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+ "weight": 1.0,
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+ "type": "ColorJitter",
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+ "kwargs": {
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+ "brightness": [
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+ 0.8,
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+ 1.2
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+ ]
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+ }
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+ },
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+ "contrast": {
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+ "weight": 1.0,
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+ "type": "ColorJitter",
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+ "kwargs": {
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+ "contrast": [
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+ 0.8,
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+ ]
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+ }
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+ },
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+ ]
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+ ]
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+ "kwargs": {
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+ ]
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+ }
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+ "affine": {
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+ "kwargs": {
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+ "translate": [
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+ ]
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+ }
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+ }
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+ }
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+ "revision": "692d1ac83fea08c85b1b1bdb1bf25e6551f8cf41",
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+ "use_imagenet_stats": true,
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+ "video_backend": "torchcodec",
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+ "return_uint8": false,
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+ "depth_output_unit": "mm",
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+ "streaming": false,
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+ "eval_split": 0.0
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+ },
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+ "env": null,
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+ "policy": {
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+ "type": "molmoact2",
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+ "n_obs_steps": 1,
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+ "input_features": {
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+ "observation.state": {
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+ "type": "STATE",
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+ "shape": [
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+ ]
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+ },
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+ "observation.images.top": {
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+ "type": "VISUAL",
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+ "shape": [
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+ 3,
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+ 480,
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+ 640
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+ ]
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+ },
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+ "observation.images.wrist": {
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+ "type": "VISUAL",
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+ "shape": [
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+ 3,
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+ 480,
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+ 640
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+ ]
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+ },
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+ "observation.images.base": {
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+ "type": "VISUAL",
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+ "shape": [
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+ 3,
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+ 480,
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+ ]
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+ }
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+ },
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+ "shape": [
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+ 7
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+ ]
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+ }
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+ },
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+ "device": "cuda",
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+ "use_amp": false,
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+ "use_peft": false,
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+ "checkpoint_path": "allenai/MolmoAct2",
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+ "chunk_size": 30,
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+ "action_mode": "continuous",
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+ "inference_action_mode": "continuous",
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+ "discrete_action_tokenizer": "allenai/MolmoAct2-FAST-Tokenizer",
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+ "discrete_generation_max_steps": null,
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+ "norm_tag": null,
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+ "norm_stats_path": null,
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+ "setup_type": "single robotic arm",
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+ "control_mode": "absolute joint pose",
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+ "image_keys": [],
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+ "normalize_language": true,
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+ "add_setup_tokens": true,
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+ "add_control_tokens": true,
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+ "normalize_gripper": true,
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+ "num_state_tokens": 256,
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+ "num_flow_timesteps": 8,
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+ "flow_matching_cutoff": 1.0,
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+ "flow_matching_time_offset": 0.001,
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+ "flow_matching_time_scale": 0.999,
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+ "num_inference_steps": null,
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+ "joint_signs": null,
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+ "train_mode_vlm": "lora",
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+ "lora_rank": 64,
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+ "lora_alpha": 16,
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+ "lora_dropout": 0.05,
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+ "lora_bias": "none",
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+ "enable_knowledge_insulation": false,
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+ "freeze_embedding": true,
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+ "gradient_checkpointing": true,
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+ "compile_model": false,
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+ "dtype": "bfloat16",
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+ "llm_residual_dropout": 0.1,
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+ "softmax_auxiliary_loss": true,
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+ "softmax_auxiliary_loss_scale": 0.0001,
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+ "discrete_loss_token_weighting": "root_subsegments_root_tokens",
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+ "optimizer_lr": 1e-05,
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+ "optimizer_vit_lr": 5e-06,
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+ "optimizer_connector_lr": 5e-06,
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+ "optimizer_action_expert_lr": 5e-05,
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+ "optimizer_betas": [
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+ 0.9,
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+ 0.95
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+ ],
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+ "optimizer_eps": 1e-06,
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+ "optimizer_weight_decay": 0.0,
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+ "scheduler_warmup_steps": 200,
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+ "scheduler_decay_steps": 6000,
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+ "scheduler_decay_lr": 1e-06,
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+ "normalization_mapping": {
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+ "ACTION": "QUANTILES",
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+ "STATE": "QUANTILES",
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+ "VISUAL": "IDENTITY"
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+ },
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+ "dataset_feature_names": {
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+ "action": [
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+ "shoulder_pan.pos",
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+ "shoulder_lift.pos",
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+ "elbow_flex.pos",
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+ "wrist_flex.pos",
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+ "wrist_roll.pos",
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+ "gripper.pos",
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+ "tilt.pos"
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+ ],
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+ "observation.state": [
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+ "shoulder_pan.pos",
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+ "shoulder_lift.pos",
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+ "elbow_flex.pos",
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+ "wrist_flex.pos",
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+ "wrist_roll.pos",
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+ "gripper.pos",
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+ "tilt.pos"
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+ ]
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+ }
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+ },
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+ "reward_model": null,
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+ "output_dir": "outputs/train/vds-smoke-20260909-gcp-molmoact2-4g-spot",
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+ "job_name": "vds-smoke-20260909-gcp-molmoact2-4g-spot",
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+ "resume": false,
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+ "tolerance_s": 0.0001,
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+ "save_checkpoint": true,
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+ "save_freq": 10,
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+ "checkpoint_format": "safetensors",
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+ "use_policy_training_preset": true,
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+ "optimizer": {
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+ "type": "molmoact2_adamw",
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+ "lr": 1e-05,
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+ "weight_decay": 0.0,
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+ "grad_clip_norm": 0.0,
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+ "betas": [
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+ ],
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+ "eps": 1e-06,
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+ "group_grad_clip_norm": 1.0
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+ },
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+ "scheduler": {
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+ "type": "molmoact2_cosine_with_warmup",
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+ "num_warmup_steps": 200,
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+ "num_decay_steps": 6000,
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+ "peak_lr": 1e-05,
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+ "decay_lr": 1e-06
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+ },
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+ "parallelism": {
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+ "dp_replicate": 4,
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+ "dp_shard": 1,
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+ "context_parallel": {
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+ "ring_degree": 1,
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+ "ulysses_degree": 1
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+ },
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+ "cfg_parallel": 1
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+ },
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+ "accelerator": {
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+ "mixed_precision": "no",
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+ "gradient_accumulation": {
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+ "steps": 1
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+ },
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+ "fsdp": {
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+ "cpu_offload": false,
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+ },
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+ "ddp": {
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+ "find_unused_parameters": true,
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+ "gradient_as_bucket_view": false,
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+ "static_graph": false
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+ },
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+ "compile": {
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+ "backend": "inductor",
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+ "mode": null,
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+ "regional": true
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+ },
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+ "activation_checkpointing": {
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+ "mode": "none"
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+ }
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+ },
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+ "eval": {
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+ "n_episodes": 50,
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+ "ema": {
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+ "power": 0.75,
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+ "min_decay": 0.0,
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+ "max_decay": 0.9999,
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+ "use_for_eval": true
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+ },
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+ "wandb": {
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+ "enable": false,
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+ "disable_artifact": false,
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+ "project": "lerobot",
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+ "console_multipart": false,
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+ "add_tags": true
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+ "image": "huggingface/lerobot-gpu:latest",
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+ "timeout": "2d",
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+ "detach": false,
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+ "tags": []
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+ },
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+ "save_checkpoint_to_hub": false,
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+ "sample_weighting": null,
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+ "rename_map": {},
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+ "checkpoint_path": null
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+ }