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Integrated task and motion planning has emerged as a challenging problem insequential decision making, where a robot needs to compute high-level strategyand low-level motion plans for solving complex tasks. While high-levelstrategies require decision making over longer time-horizons and scales, theirfeasibility depends on low-level constraints based upon the geometries andcontinuous dynamics of the environment. The hybrid nature of this problem makesit difficult to scale; most existing approaches focus on deterministic, fullyobservable scenarios. We present a new approach where the high-level decisionproblem occurs in a stochastic setting and can be modeled as a Markov decisionprocess. In contrast to prior efforts, we show that complete MDP policies, orcontingent behaviors, can be computed effectively in an anytime fashion. Ouralgorithm continuously improves the quality of the solution and is guaranteedto be probabilistically complete. We evaluate the performance of our approachon a challenging, realistic test problem: autonomous aircraft inspection. Ourresults show that we can effectively compute consistent task and motionpolicies for the most likely execution-time outcomes using only a fraction ofthe computation required to develop the complete task and motion policy.

An Anytime Algorithm for Task and Motion MDPs