README.md

metadata

license: cc-by-nc-4.0

DexWM: World Models for Learning Dexterous Hand-Object Interactions from Human Videos

📄 Paper   |   💻 Code   |   🌐 Project Page

Description

This dataset contains the RoboCasa simulation data used in DexWM: World Models for Learning Dexterous Hand-Object Interactions from Human Videos. It includes two data regimes for training and evaluation of DexWM.

• RoboCasa Random: Contains exploratory_movement and gripper_open_and_close sequences. These are random interaction trajectories collected using a Franka arm with an Allegro hand, used for model fine-tuning.
• Pick-and-Place: Contains the pick-and-place-2.0 dataset, used exclusively for evaluating manipulation performance.

All data is stored in .hdf5 format, where each file contains sequential robot interaction trajectories, including states and actions for dexterous manipulation.

Citation

@article{goswami2025dexwm,
  title={World Models for Learning Dexterous Hand-Object Interactions from Human Videos},
  author={Goswami, Raktim Gautam and Bar, Amir and Fan, David and Yang, Tsung-Yen and Zhou, Gaoyue and Krishnamurthy, Prashanth and Rabbat, Michael and Khorrami, Farshad and LeCun, Yann},
  journal={arXiv preprint arXiv:2512.13644},
  year={2026}
}