面向机器人空气曲棍球快速轨迹优化的接触规划蒸馏
Distilling Contact Planning for Fast Trajectory Optimization in Robot Air Hockey
AI总结：
针对机器人空气曲棍球高动态接触控制难实时求解的问题，提出高层接触规划随机最优控制策略蒸馏结合底层在线模型预测控制的框架，可平衡击球精度与冰球速度，在模拟和真实比赛中性能优于纯控制及纯学习方法。
摘要：
通过接触进行机器人控制极具挑战性，因为它需要在长时域和不连续系统动力学上进行推理。空气曲棍球这类高动态任务还要求敏捷行为，使得对应的最优控制问题难以实时规划求解。基于学习的方法通过将计算成本高昂的接触推理转移到离线学习阶段来解决这一问题。然而，若期望在运动学和动力学约束附近运行，学习受这些约束约束的底层电机策略会很困难。本文探索了将用于高层接触规划的随机最优控制策略蒸馏，与用于底层约束运动规划的在线模型预测控制相结合的方法。我们的系统通过利用撞库反弹球（bank shots）和机器人的运动学结构，学会平衡击球精度和产生的冰球速度。我们表明，所提出的框架在机器人空气曲棍球的模拟和真实比赛中，均优于纯基于控制和纯基于学习的技术。
英文摘要：
Robot control through contact is challenging as it requires reasoning over long horizons and discontinuous system dynamics. Highly dynamic tasks such as Air Hockey additionally require agile behavior, making the corresponding optimal control problems intractable for planning in realtime. Learning-based approaches address this issue by shifting computationally expensive reasoning through contacts to an offline learning phase. However, learning low-level motor policies subject to kinematic and dynamic constraints can be challenging if operating in proximity to such constraints is desired. This paper explores the combination of distilling a stochastic optimal control policy for high-level contact planning and online model-predictive control for low-level constrained motion planning. Our system learns to balance shooting accuracy and resulting puck speed by leveraging bank shots and the robot's kinematic structure. We show that the proposed framework outperforms purely control-based and purely learning-based techniques in both simulated and real-world games of Robot Air Hockey.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
