Energy-based Contact Planning under Uncertainty for Robot Air Hockey
<|reference_start|>Energy-based Contact Planning under Uncertainty for Robot Air Hockey: Planning robot contact often requires reasoning over a horizon to anticipate outcomes, making such planning problems computationally expensive. In this letter, we propose a learning framework for efficient contact planning in real-time subject to uncertain contact dynamics. We implement our approach for the example task of robot air hockey. Based on a learned stochastic model of puck dynamics, we formulate contact planning for shooting actions as a stochastic optimal control problem with a chance constraint on hitting the goal. To achieve online re-planning capabilities, we propose to train an energy-based model to generate optimal shooting plans in real time. The performance of the trained policy is validated %in experiments both in simulation and on a real-robot setup. Furthermore, our approach was tested in a competitive setting as part of the NeurIPS 2023 Robot Air Hockey Challenge.<|reference_end|>
@article{jankowski2024energy-based,
    title={Energy-based Contact Planning under Uncertainty for Robot Air Hockey},
    author={Julius Jankowski, Ante Mari'c, Puze Liu, Davide Tateo, Jan Peters,
  and Sylvain Calinon},
    journal={arXiv preprint arXiv:2407.03705},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03705},
    primaryClass={cs.RO}
}