MultiPush：利用类车推动机器人团队学习重排

中文摘要 AI 辅助

我们关注在受限工作空间内，通过使用一组类车机器人进行推动来重排多个物体的问题。虽然使用多个机器人有可能提高执行效率，但冲突解决的需求以及由物理、机器人设计和工作空间边界产生的运动学约束使这一问题尤其具有挑战性。我们的关键见解是，通过利用该领域类车运动学引入的结构，我们可以将问题松弛为对机器人的Dubins曲线有序分配。为此，我们引入了MultiPush，一个基于强化学习的框架，通过利用约束感知的可通行图，共同确定推动任务的高效调度及其对可用机器人的分配。在多达14个物体和由两到四个机器人组成的团队的广泛模拟试验中，与基线相比，MultiPush将完工时间减少了最多16%，同时所需的规划时间最多快2.9倍。我们在一个真实场景中演示了MultiPush，涉及在受限空间中由两个和三个机器人（1/10比例赛车）重排12个物体。

英文摘要

We focus on the problem of rearranging multiple objects within a constrained workspace via pushing using a team of car-like robots. While the use of multiple robots offers the potential for more efficient execution, the need for conflict resolution and the kinematic constraints arising from physics, robot design, and the workspace boundary make this problem especially challenging. Our key insight is that by exploiting the structure introduced by the car-like kinematics of the domain, we could relax the problem into an ordered assignment of Dubins curves to robots. To this end, we introduce MultiPush, a reinforcement-learning based framework that jointly determines an efficient schedule of pushing tasks and their allocation to available robots by leveraging a constraint-aware traversability graph. Across extensive simulated trials with up to 14 objects and teams of two to four robots, MultiPush reduces the makespan by up to 16% compared to the baselines while requiring up to 2.9 times faster planning time. We demonstrate MultiPush on a real-world scenario involving the rearrangement of 12 objects by two and three robots (1/10-scale racecars) in a constrained space.

发表机构

• University of Michigan（密歇根大学）

机构由 AI 辅助整理，请以论文原文为准。