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1# Example code to load safetensors
2from transformers import AutoModel
3
4model = AutoModel.from_pretrained("asRobotics/fingernet", trust_remote_code=True)
5x = torch.zeros((1, 6)) # Example input: batch size of 1, 6D motion
6output = model(x)1# Example code to load onnx
2import onnxruntime as ort
3import numpy as np
4from huggingface_hub import hf_hub_download
5
6onnx_model_path = hf_hub_download(repo_id="asRobotics/fingernet", filename="model.onnx")
7ort_session = ort.InferenceSession(onnx_model_path)
8x = np.zeros((1, 6)).astype(np.float32) # Example input: batch size of 1, 6D motion
9outputs = ort_session.run(None, {"motion": x})1@article{liu2024proprioceptive,
2 title={Proprioceptive learning with soft polyhedral networks},
3 author={Liu, Xiaobo and Han, Xudong and Hong, Wei and Wan, Fang and Song, Chaoyang},
4 journal={The International Journal of Robotics Research},
5 volume = {43},
6 number = {12},
7 pages = {1916-1935},
8 year = {2024},
9 publisher={SAGE Publications Sage UK: London, England},
10 doi = {10.1177/02783649241238765}
11}1@article{wu2025magiclaw,
2 title={MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap},
3 author={Wu, Tianyu and Han, Xudong and Sun, Haoran and Zhang, Zishang and Huang, Bangchao and Song, Chaoyang and Wan, Fang},
4 journal={arXiv preprint arXiv:2509.19169},
5 year={2025}
6}