Co-optimizing task and motion planning

Abstract

Solutions to robotic manipulation problems can be substantially improved through integrated task and motion planning. Existing approaches typically focus on satisfaction, finding a feasible solution, instead of optimization. We formulate large-scale robotic manipulation problems as multi-level optimization, incorporating task, action, and motion planning. We develop an integrated planning approach for solving this optimization problem and generating a combined motion plan for a robot to optimize a task-level objective. This approach utilizes a combinatorial search algorithm for task planning and incrementally exploits information from lower-level optimization to improve the high-level task plan. Empirical results show that this integrated approach not only significantly outperforms a traditional top-down approach in solution quality, but also avoids infeasible lower-level motion plans.