外观
论文arXiv 2610.0629023 天前
GAMBIT：学习规划连续多机器人轨迹
原文链接：GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories（arXiv 2610.06290 · 发布日期：2026-10-05）
GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
GAMBIT面向多机器人连续轨迹执行，结合模仿学习、强化学习和安全回滚，在连续域实验中验证协调规划能力。
核心要点
- 密集交互环境中，单个机器人需牺牲局部最优以提升团队整体表现。
- 方法在运动基元上先模仿学习协调选择，再用强化学习微调策略。
- 安全回滚机制提供备用轨迹，保证执行过程中始终无碰撞。
- 实验显示其优于集中式与分散式基线，可协调上千机器人且延迟低于数百毫秒。
验证范围与局限：验证范围聚焦双积分器连续动力学下的连续域多机器人轨迹执行。
英文摘要 (Abstract)
GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously, in multi-robot coordination, individual robots may need to forgo locally reward-maximising behaviours to improve overall team performance. Such self-sacrificial behaviours are difficult to capture with manually designed heuristics, particularly in dense, interaction-rich environments. Focusing on double-integrator continuous dynamics, this work studies how to learn such coordinated heuristics over motion primitives for multi-robot trajectory execution. Our framework, GAMBIT, first learns coordinated motion-primitive selection through imitation learning and subsequently fine-tunes the policy through reinforcement learning. We further introduce a safeguarded rollout mechanism with backup trajectories that guarantees collision-free execution at all times. Experiments demonstrate that GAMBIT substantially outperforms a range of baselines, including centralised motion planners and decentralised reactive planners, while exhibiting strong scalability. In particular, it coordinates over a thousand robots with planning latency below a few hundred milliseconds in continuous domains.
作者：Rishabh Jain, Akmaral Moldagalieva, Lorenzo Magnino, Michael Amir, Keisuke Okumura, Ajay Shankar, Wolfgang Hönig, Amanda Prorok
来源
本页事实对应的公开页面，以原文为准。