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.Project page:http://cyc0429.github.io/sai-project-page/

翻译：协同移动操作需要机器人与部分可观测的伙伴协调，同时通过共享物体进行物理交互。这一任务具有挑战性，因为失败往往并非源于局部技能的不足，而是由于不恰当的等待、让步、牵引、释放或重新定位行为。我们研究了两台双臂移动机器人通过刚性及柔性物体耦合的问题，并提出序列非对称模仿（Sequential Asymmetric Imitation, SAI）——一种无需同步双人演示或显式机器人间通信的单人遥操作课程，用于学习耦合多机器人行为。SAI首先通过顺从人类伙伴的单侧演示训练机器人A，随后将机器人A的策略部署后训练机器人B，最后通过稀疏干预（在协作失败点附近）优化机器人A。这一分阶段训练过程使策略逐渐暴露于更真实的伙伴行为，包括延迟、相位失配、让步不足及交互冲突。在真实世界双臂机器人操作任务中，相比独立模仿与课程消融基线方法，SAI提升了任务成功率、相位同步性及针对伙伴的让步能力。这些结果表明，物理耦合协作可通过模仿课程的结构而非同步多操作者演示或显式协调机制来学习。项目页面：http://cyc0429.github.io/sai-project-page/