Publikation
Energy-based Contact Planning under Uncertainty for Robot Air Hockey
Julius Jankowski; Ante Maric; Puze Liu; Davide Tateo; Jan Peters; Sylvain Calinon
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2407.03705, Pages 1-7, arXiv, 2024.
Zusammenfassung
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 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