FingerNet-100K

This dataset contains 100K samples of data for FingerNet, generated by finite element simulations.

Dataset Schema

There are two subsets in this dataset:

• finger: Contains 100,000 samples of typical asFinger.
• finger_surf: Contains 100,000 samples of asFinger with a contact surface.

Each sample in this dataset contains three components:

Field Name	Type	Shape	Description
motion	List[float64]	[6]	The 6D motion of the finger, including translation (dx, dy, dz) and rotation (rx, ry, rz) in mm and rad.
force	List[float64]	[6]	The 6D force and torque on the bottom surface of the finger, corresponding to (fx, fy, fz, tx, ty, tz) in N and Nmm.
nodes	List[List[float64]]	[N,3]	The 3D displacement of N surface nodes of the finger, where each node is represented as [dx, dy, dz] in mm.

Usage

from datasets import load_dataset

dataset = load_dataset("asRobotics/fingernet-100k")

# Access the 'finger' subset
for sample in dataset['finger']:
    motion = sample['motion']
    force = sample['force']
    nodes = sample['nodes']

# Access the 'finger_surf' subset
for sample in dataset['finger_surf']:
    motion = sample['motion']
    force = sample['force']
    nodes = sample['nodes']

Citation

If you use this model in your research, please cite the following papers:

@article{liu2024proprioceptive,
  title={Proprioceptive learning with soft polyhedral networks},
  author={Liu, Xiaobo and Han, Xudong and Hong, Wei and Wan, Fang and Song, Chaoyang},
  journal={The International Journal of Robotics Research},
  volume = {43},
  number = {12},
  pages = {1916-1935},
  year = {2024},
  publisher={SAGE Publications Sage UK: London, England},
  doi = {10.1177/02783649241238765}
}

@article{wu2025magiclaw,
  title={MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap},
  author={Wu, Tianyu and Han, Xudong and Sun, Haoran and Zhang, Zishang and Huang, Bangchao and Song, Chaoyang and Wan, Fang},
  journal={arXiv preprint arXiv:2509.19169},
  year={2025}
}