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This research presents Sequential Asymmetric Imitation (SAI), a novel curriculum that enables two mobile manipulators to learn coupled collaboration policies using only unilateral human demonstrations. By structuring the training process to expose agents to realistic coordination failures like mistimed yielding, the method eliminates the need for synchronized dual-operator inputs or explicit inter-robot communication protocols. The approach demonstrates improved task success and phase synchronization in real-world scenarios where robots physically interact through shared objects.
Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
arXiv:2606.16490v1 Announce Type: new Abstract: 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/
Abstract: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 this http URL page:this http URL
Robotics (cs.RO)
arXiv:2606.16490 [cs.RO]
(or arXiv:2606.16490v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16490 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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