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Solving complex robot manipulation tasks requires a Task and Motion Planner (TAMP) that searches for a sequence of symbolic actions, i.e. a task plan, and also computes collision-free motion paths. As the task planner and the motion planner are closely interconnected TAMP is considered a challenging problem. In this paper, a Probabilistic Integrated Task and Motion Planner (PROTAMP-RRT) is presented. The proposed method is based on a unified Rapidly-exploring Random Tree (RRT) that operates on both the geometric space and the symbolic space. The RRT is guided by the task plan and it is enhanced with a probabilistic model that estimates the probability of sampling a new robot configuration towards the next sub-goal of the task plan. When the RRT is extended, the probabilistic model is updated alongside. The probabilistic model is used to generate a new task plan if the feasibility of the previous one is unlikely. The performance of PROTAMP-RRT was assessed in simulated pick-and-place tasks, and it was compared against state-of-the-art approaches TM-RRT and Planet, showing better performance.

PROTAMP-RRT: A Probabilistic Integrated Task and Motion Planner Based on RRT