Robotics: Science and Systems XII

Incremental Task and Motion Planning: A Constraint-Based Approach

Neil T. Dantam, Zachary K. Kingston, Swarat Chaudhuri, Lydia E. Kavraki


We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstrac- tions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved per- formance and generality compared to a similar, state-of-the- art, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.



    AUTHOR    = {Neil T. Dantam AND Zachary K. Kingston AND Swarat Chaudhuri AND Lydia E. Kavraki}, 
    TITLE     = {Incremental Task and Motion Planning: A Constraint-Based Approach}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2016}, 
    ADDRESS   = {AnnArbor, Michigan}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2016.XII.002}