2025-09-04 ユニバーシティ・カレッジ・ロンドン（UCL）
Robotic arms used in study. Credit: Google Deepmind/UCL.
＜関連情報＞
- https://www.ucl.ac.uk/news/2025/sep/robots-learn-work-together-well-choreographed-dance
- https://www.science.org/doi/10.1126/scirobotics.ads1204
ロボバレエ：グラフニューラルネットワークと強化学習によるマルチロボット到達計画 RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning
Matthew Lai, Keegan Go, Zhibin Li, Torsten Kröger, […] , and Jonathan Scholz
Science Robotics Published:3 Sep 2025
DOI:https://doi.org/10.1126/scirobotics.ads1204
Abstract
Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatiotemporal constraints remain computationally intractable for classical methods at real-world scales. Existing multiarm systems deployed in industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally generated environments with diverse obstacle layouts, robot configurations, and task distributions. It uses a graph representation of scenes and a graph policy neural network trained through RL to generate trajectories of multiple robots, jointly solving the subproblems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to capabilities such as fault-tolerant planning and online perception-based replanning, where rapid adaptation to dynamic task sets is required.
