论文标题
学会纠正错误：长途任务和运动计划中的重跳跃
Learning to Correct Mistakes: Backjumping in Long-Horizon Task and Motion Planning
论文作者
论文摘要
随着机器人越来越有能力操纵和长期自治，长期任务和运动计划问题变得越来越重要。此类问题的一个关键挑战是，计划中的早期行动可能使未来的行动变得不可行。在搜索中达到末端时，大多数现有的计划者都会使用回溯，从而详尽地重新评估了运动级别的动作，通常会导致效率低下的计划，尤其是当搜索深度较大时。在本文中，我们建议学习反式启发式方法，这些启发式方法可以使用监督的学习模型直接识别罪魁祸首，以指导任务级别的搜索。根据对两个不同任务的评估，我们发现我们的方法显着提高了与回溯相比的计划效率，并且还将其推广到新的对象数量的问题。
As robots become increasingly capable of manipulation and long-term autonomy, long-horizon task and motion planning problems are becoming increasingly important. A key challenge in such problems is that early actions in the plan may make future actions infeasible. When reaching a dead-end in the search, most existing planners use backtracking, which exhaustively reevaluates motion-level actions, often resulting in inefficient planning, especially when the search depth is large. In this paper, we propose to learn backjumping heuristics which identify the culprit action directly using supervised learning models to guide the task-level search. Based on evaluations on two different tasks, we find that our method significantly improves planning efficiency compared to backtracking and also generalizes to problems with novel numbers of objects.
