MultiPush: 車型ロボットチームによる押し操作での物体再配置学習
MultiPush: Learning to Rearrange with Teams of Car-Like Pushers
複数の車型ロボットが協調して物体を押し、制約空間内で効率的に再配置する強化学習フレームワークを提案。タスク割当とスケジューリングを同時最適化し、実機でも検証した。
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著者: Jeeho Ahn, Christoforos Mavrogiannis
分類: cs.RO
原文アブストラクト
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.