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

• Published in: IEEE robotics and automation letters Vol. 8; no. 12; pp. 8398 - 8405
• Main Authors: Saccuti, Alessio, Monica, Riccardo, Aleotti, Jacopo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-12-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collision avoidance; Generators; manipulation plan- ning; Motion planning; Pick and place tasks; Planning; Probabilistic logic; Probabilistic models; Probability theory; Robots; Statistical analysis; Streams; Symbols; Task analysis; Task and motion planning; Task complexity
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2023.3327657

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.