Task and Motion Planning for Execution in the Real

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 like 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 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 is 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.

任务与运动规划代表了一套强大的混合规划方法，它将离散任务领域的推理和连续运动生成相结合。传统推理需要任务领域模型以及足够的信息，以便将动作基于运动规划查询。这种知识的缺失常常源于遮挡或不精确建模等原因。这项工作生成的任务和运动规划包含在规划时无法完全基于实际情况的动作。在执行过程中，这样的动作由提供的人工设计或学习到的闭环行为来处理。执行过程将离线规划的运动和在线行为相结合，直至达到任务目标。行为的失败作为约束反馈回来，以寻找新的规划。进行了40次真实机器人试验和激励性演示，以评估所提出的框架，并与现有技术进行比较。结果显示，在出现各种知识缺失的问题中，执行时间更快、动作数量更少且成功率更高。实验数据已共享，供研究人员模拟这些设置。这项工作在扩展机器人能够处理的现实的部分基于实际情况的问题的适用类别方面显示出了前景。