Hierarchical Planner with Composable Action Models for Asynchronous Parallelization of Tasks and Motions

Abstract

Task and motion planning is a relevant yet hard to solve problem in robotic manipulation. Large number of degrees of freedom with multiple manipulators and several objects require specialized algorithms, which can deal with the hybrid planning and optimization problem. An additional challenge is the asynchronous parallelization of single robot actions on interacting manipulators. In this paper we propose a system with a hierarchical planner, which solves the task and motion problem and optimizes for a subsequent parallelization. We use action models based on a constraint formulation; thus, the execution engine can parallelize the sequential plan without synchronization between different tasks. In the experiment, we solve a task and motion problem with difficult geometric constraints and combinatorial complexity. The asynchronously parallel execution of that plan is demonstrated on a real world dual-arm robot.