In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.
翻译:为了解决复杂、长期的横向任务,智能机器人需要与运动规划一起进行高层次的抽象规划和推理,但抽象模型通常失灵,使用这些模型计算的计划或政策可能无法执行。这些问题在机器人需要了解和规划多种紧急情况的随机情况下更加严重。我们提出了在随机环境中综合任务和运动规划的新方法。与以前朝这个方向开展的工作相比,我们表明,我们的方法可以有效地计算综合任务和运动政策,其分支结构将处理多个执行时紧急情况的代理行为编码。我们证明,我们的算法是概率性完整的,可以随时计算可行的解决方案政策,这样,在时间上遇到未解决的意外事件的可能性就会减少。一系列具有挑战性的问题的实证结果显示了我们方法的效用和范围。