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.

该研究通过组合对离散任务领域和连续运动生成进行推理的混合规划方法，生成包含无法完全基于计划时间进行植入的行为的任务和运动计划，在执行过程中使用由人设计或学习的闭环行为处理此类行为，并通过反馈行为故障作为约束条件找到新的计划，从而在真实机器人试验中展示了更快的执行时间、更少的动作数量以及在出现多样化间隙问题时更高的成功率，并与最新技术进行了比较，提供实验数据供研究人员模拟这些情景，为机器人解决更多现实部分植入问题的适用类别展示了希望。