arXiv:2606.16490cs.RO2026-06被引 1

通过分阶段单操作员训练,让机器人学会协作时的时机配合与动作调整。

Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies

论文配图:Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
图 1 · 摘自论文原文
  • 用单人示范+逐步迭代方式训练双机器人协同行为
  • 真实任务中成功率提升,动作同步性与让步反应更自然
  • 无需同步双人演示或通信,适合物理耦合协作场景

协作式移动操作需在部分可观测伙伴下通过共享物体进行物理交互。失败常源于时机不当的等待、让位、拉拽、释放或重定位,而非局部技能差。本文研究两个双臂移动机器人通过刚性与柔性物体耦合的协作问题。提出顺序不对称模仿(SAI):先由单人类操作者示范训练机器人A,再以已部署的机器人A策略为对手训练机器人B,最后通过稀疏干预在协调失败处优化机器人A。该分阶段过程使策略逐步接触真实伙伴行为,如延迟、相位错位、让位不足和交互冲突。在真实双机器人操作任务中,SAI显著提升任务成功率、相位同步性和对伙伴的响应性,优于独立模仿与课程消融基线。结果表明,物理耦合协作可通过模仿课程结构实现,无需同步双人示范或显式协调机制。

原文摘要 · Abstract (English)

Collaborative mobile manipulation requires robots to coordinate with a partially observed partner while physically interacting through shared objects. This is difficult because failures often arise not from poor local skills, but from mistimed waiting, yielding, pulling, releasing, or repositioning. We study this problem with two bimanual mobile manipulators coupled through rigid and deformable objects. We propose Sequential Asymmetric Imitation (SAI), a single-teleoperator curriculum for learning coupled multi-robot behaviors without synchronized dual-operator demonstrations or explicit inter-robot communication. SAI trains Robot A from unilateral demonstrations with a compliant human partner, trains Robot B against the deployed Robot A policy, and then refines Robot A using sparse interventions near coordination failures. This staged process exposes the policies to increasingly realistic partner behaviors, including delay, phase mismatch,insufficient yielding, and interaction conflict. Across real-world dual-robot manipulation tasks, SAI improves task success, phase synchronization, and partner-contingent yielding over independent imitation and curriculum-ablation baselines. These results suggest that physically coupled collaboration can be learned through the structure of the imitation curriculum, rather than through synchronized multi-operator demonstrations or explicit coordination mechanisms. More videos on project page:http://cyc0429.github.io/sai-project-page/

机器人协作模仿学习多机协同

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。