Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement
This addresses the challenge of efficient dual-arm robot coordination for complex tabletop tasks, which is incremental as it builds on existing rearrangement planning methods.
The paper tackled the problem of coordinating two robot arms for non-monotone tabletop object rearrangement, where objects may need to be moved multiple times and handed off between arms, and showed that their task planning algorithms achieve significant time savings compared to greedy approaches and naive parallelization.
We investigate the problem of coordinating two robot arms to solve non-monotone tabletop multi-object rearrangement tasks. In a non-monotone rearrangement task, complex object-object dependencies exist that require moving some objects multiple times to solve an instance. In working with two arms in a large workspace, some objects must be handed off between the robots, which further complicates the planning process. For the challenging dual-arm tabletop rearrangement problem, we develop effective task planning algorithms for scheduling the pick-n-place sequence that can be properly distributed between the two arms. We show that, even without using a sophisticated motion planner, our method achieves significant time savings in comparison to greedy approaches and naive parallelization of single-robot plans.