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.

翻译：机器人接触规划通常需要在时间跨度上进行推理以预测结果，这使得此类规划问题计算成本高昂。在本信中，我们提出一种学习框架，用于在不确定接触动力学条件下实现高效的实时接触规划。我们以机器人空气曲棍球任务为例实施该方法。基于学习得到的冰球动力学随机模型，我们将击球动作的接触规划表述为带有命中球门机会约束的随机最优控制问题。为实现在线重规划能力，我们提出训练一个基于能量的模型来实时生成最优击球方案。训练策略的性能在仿真和真实机器人实验中得到验证。此外，我们的方法作为NeurIPS 2023机器人空气曲棍球挑战赛的组成部分，已在竞技环境中通过测试。