Datasets:

asRobotics
/

fingernet-100k

license: bsd-3-clause
task_categories:
  - robotics
tags:
  - fingernet
  - asfinger
modalities:
  - tabular
configs:
  - config_name: finger
    data_files: data/finger/data_*.parquet
  - config_name: finger_surf
    data_files: data/finger_surf/data_*.parquet
dataset_info:
  - config_name: finger
    features:
      - name: motion
        list: float64
      - name: force
        list: float64
      - name: nodes
        list:
          list: float64
  - config_name: finger_surf
    features:
      - name: motion
        list: float64
      - name: force
        list: float64
      - name: nodes
        list:
          list: float64
size_categories:
  - 100K<n<1M

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}
}