Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources such as occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare it against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data are shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.

中文翻译：

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真实执行的任务和运动规划


任务和运动规划代表了一组强大的混合规划方法，它将离散任务域的推理和连续运动生成结合起来。传统推理需要任务域模型和足够的信息来为运动规划查询提供基础操作。这方面知识的差距通常源于遮挡或不精确的建模等原因。这项工作生成任务和行动计划，其中包括在计划时无法完全落实的行动。在执行过程中，此类操作由提供的人工设计或学习的闭环行为来处理。执行将离线计划的动作和在线行为相结合，直到达到任务目标。行为失败会被反馈为寻找新计划的约束。进行了四十次真实机器人试验和激励演示，以评估所提出的框架并将其与最先进的框架进行比较。结果显示，执行时间更快，操作数量更少，并且在出现不同差距的问题上更成功。实验数据可供研究人员共享以模拟这些设置。这项工作有望扩大机器人可以解决的现实部分接地问题的适用范围。