MoDex: Planning High-Dimensional Dexterous Control via Learning Neural Hand Models
<|reference_start|>MoDex: Planning High-Dimensional Dexterous Control via Learning Neural Hand Models: Controlling hands in the high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration from the human embodied cognition and reconsider dexterous hands as learnable systems. Specifically, we introduce MoDex, a framework which employs a neural hand model to capture the dynamical characteristics of hand movements. Based on the model, a bidirectional planning method is developed, which demonstrates efficiency in both training and inference. The method is further integrated with a large language model to generate various gestures such as ``Scissorshand" and ``Rock\&Roll." Moreover, we show that decomposing the system dynamics into a pretrained hand model and an external model improves data efficiency, as supported by both theoretical analysis and empirical experiments. Additional visualization results are available at https://tongwu19.github.io/MoDex.<|reference_end|>
@article{wu2024modex:,
    title={MoDex: Planning High-Dimensional Dexterous Control via Learning Neural
  Hand Models},
    author={Tong Wu, Shoujie Li, Chuqiao Lyu, Kit-Wa Sou, Wang-Sing Chan, Wenbo
  Ding},
    journal={arXiv preprint arXiv:2409.10983},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10983},
    primaryClass={cs.RO}
}