Instructions to use jstm/molmoact2_mpc_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use jstm/molmoact2_mpc_50 with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
MolmoAct2 MPC N=50
MolmoAct2 (allenai/MolmoAct2-LIBERO) finetuned on motion-planned ManiSkill MPC tabletop demos with absolute end-effector pose control (pd_ee_pose).
- Dataset:
jstm/mpc_lerobot_pd_ee_pose_50 - Checkpoint: training step 10 000
- Control mode:
pd_ee_pose - Cameras:
camera_center,camera_left,camera_wrist(378×378)
Demonstrations
| Env | Demonstrations |
|---|---|
| PickCube-v2-wrist | 48 |
| PushCube-v2 | 48 |
| LiftPegUpright-v2 | 48 |
| PullCubeTool-v2 | 48 |
| Total | 192 |
Target collection size was N=50 successes per env; 48 successful demos were retained per env after motion planning / replay.
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