aiqbench analysis
This research introduces Sequential Asymmetric Imitation (SAI), a novel curriculum that enables two mobile manipulators to learn coupled collaboration policies using only unilateral human demonstrations. By iteratively refining robot behaviors against increasingly realistic partner delays and conflicts, the method achieves superior task success and synchronization without requiring explicit inter-robot communication or synchronized dual-operator inputs. The findings demonstrate that complex multi-robot coordination can emerge from structured imitation processes rather than relying on pre-defined coordination mechanisms.
Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
arXiv:2606.16490v2 Announce Type: replace 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. More videos on 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 mechanisms. More videos on project page:this http URL
Robotics (cs.RO)
arXiv:2606.16490 [cs.RO]
(or arXiv:2606.16490v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16490 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 272 words
Related coverage
Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies
arXiv · Jun 16, 2026
EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation
arXiv · Oct 6, 2026
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
arXiv · Jun 17, 2026
GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training
arXiv · Jul 21, 2026