Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAMP problem formulation assumes full observability and deterministic action effects. These assumptions limit the ability of the planner to gather information and make decisions that are risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes. Our planner reasons under uncertainty at both the abstract task level and continuous controller level. Given a set of closed-loop goal-conditioned controllers operating in the primitive action space and a description of their preconditions and potential capabilities, we learn a high-level abstraction that can be solved efficiently and then refined to continuous actions for execution. We demonstrate our approach on several robotics problems where uncertainty is a crucial factor and show that reasoning under uncertainty in these problems outperforms previously proposed determinized planning, direct search, and reinforcement learning strategies. Lastly, we demonstrate our planner on two real-world robotics problems using recent advancements in probabilistic perception.

翻译：集成式任务与运动规划（TAMP）已被证明是解决可泛化长周期机器人操作与导航问题的有效方法。然而，典型的TAMP问题定义假设完全可观测性和确定性动作效果，这些假设限制了规划器收集信息并做出风险感知决策的能力。我们提出一种面向不确定性与风险感知的TAMP策略（TAMPURA），该策略能够高效求解包含初始状态与动作结果不确定性的长周期规划问题，包括需要信息收集以及规避不良且不可逆结果的问题。我们的规划器在抽象任务层级和连续控制器层级均能进行不确定性推理。给定一组在原始动作空间中运行的闭环目标导向控制器及其前置条件和潜在能力描述，我们学习一个可高效求解的高层抽象，随后将其细化为连续动作以执行。我们在多个以不确定性为关键因素的机器人问题上验证了该方法，结果表明在这些问题中进行不确定性推理优于先前提出的确定性规划、直接搜索和强化学习策略。最后，我们利用概率感知领域的最新进展，在两个真实机器人问题上展示了该规划器的性能。