[2605.29298] MonoDuo: Using One Robot Arm to Learn Bimanual Policies
[Submitted on 28 May 2026]
Title:MonoDuo: Using One Robot Arm to Learn Bimanual Policies
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Abstract:Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual robots and datasets. Single-arm robots, however, are widely available in research labs. Can we leverage them to train bimanual robot policies? We present MonoDuo, a framework for learning bimanual manipulation policies using single-arm robot demonstrations paired with human collaboration. MonoDuo collects data by teleoperating a single-arm robot to perform one side of a bimanual task while a human performs the other, then swapping roles to cover both sides. RGB-D observations from a wrist-mounted and fixed camera are augmented into synthetic demonstrations for target bimanual robots using state-of-the-art hand pose estimation, image and point cloud segmentation, and inpainting. These synthetic demonstrations, grounded in real robot kinematics, are used to train bimanual policies. We evaluate MonoDuo on five tasks: box lifting, backpack packing, cloth folding, jacket zipping, and plate handover. Compared to approaches relying solely on human bimanual videos, MonoDuo enables zero-shot deployment on unseen bimanual robot configurations, achieving success rates up to 70%. With only 25 target robot demonstrations, few-shot finetuning further boosts success rates by 65-70% over training from scratch, demonstrating MonoDuo's effectiveness in efficiently transferring knowledge from single-arm robot data to bimanual robot policies.
Comments: Accepted to appear in the 2026 IEEE International Conference on Robotics and Automation (ICRA), Vienna, Austria, 1-5 June 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.29298 [cs.RO]
(or arXiv:2605.29298v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29298
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sandeep Bajamahal [view email] [v1] Thu, 28 May 2026 03:27:38 UTC (11,524 KB)
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