An Anytime Hierarchical Approach for Stochastic Task and Motion Planning
This addresses the challenge of reliable robot planning in uncertain real-world scenarios, representing an incremental advance over prior work in stochastic task and motion planning.
The paper tackles the problem of integrated task and motion planning for robots in stochastic environments, where abstract models can lead to inexecutable plans. It presents a new approach that computes policies handling multiple contingencies, proving probabilistic completeness and showing decreasing failure probability over time in empirical tests.
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.