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The paper addresses a practical bottleneck in imitation learning: collecting many high-quality robot demonstrations without exhausting human operators. Its semi-autonomous approach, where the robot runs most of each trajectory and the operator makes only fine corrections at key stages, is a workflow change more than a new learning algorithm. Readers should check whether the reported gains hold beyond the single peg-in-hole task and how the data-collection savings compare against the cost of building the MR setup.
Scalable, Intuitive, Mixed Reality-Based Semi-Autonomous Robotic Telemanipulation for the Execution of a Fine-Grained Bimanual Task
arXiv:2610.25006v1 Announce Type: new Abstract: Nowadays, robotic telemanipulation enables humans to control robots so as to execute tasks in a manner similar to how they perform them, and serves as an essential approach for collecting on-robot data for robot learning by human demonstrations. The quality of collected demonstrations directly impacts the performance of the learned policy. However, existing telemanipulation systems rely heavily on human operation, particularly for large-scale data collection. Collecting dozens or hundreds of demonstrations is a demanding process, as operators tend to fatigue quickly, especially during the execution of dexterous tasks that require sustained concentration. Recently, semi-autonomous robotic telemanipulation has emerged as a promising direction that reduces operator workload while preserving human-in-the-loop control for task execution. We therefore introduce a scalable, intuitive, semi-autonomous robotic telemanipulation system that leverages a mixed reality (MR) headset for on-robot data collection. The system autonomously executes the majority of the task trajectories and only requires the operator to make fine adjustments at key stages. We validate the effectiveness of our system by collecting data and training imitation learning policies on the execution of a high-precision, fine-grained bimanual peg-in-hole task. With lower data collection effort, our system obtains high-quality demonstrations that can be used to train fully autonomous task execution policies.
Abstract:Nowadays, robotic telemanipulation enables humans to control robots so as to execute tasks in a manner similar to how they perform them, and serves as an essential approach for collecting on-robot data for robot learning by human demonstrations. The quality of collected demonstrations directly impacts the performance of the learned policy. However, existing telemanipulation systems rely heavily on human operation, particularly for large-scale data collection. Collecting dozens or hundreds of demonstrations is a demanding process, as operators tend to fatigue quickly, especially during the execution of dexterous tasks that require sustained concentration. Recently, semi-autonomous robotic telemanipulation has emerged as a promising direction that reduces operator workload while preserving human-in-the-loop control for task execution. We therefore introduce a scalable, intuitive, semi-autonomous robotic telemanipulation system that leverages a mixed reality (MR) headset for on-robot data collection. The system autonomously executes the majority of the task trajectories and only requires the operator to make fine adjustments at key stages. We validate the effectiveness of our system by collecting data and training imitation learning policies on the execution of a high-precision, fine-grained bimanual peg-in-hole task. With lower data collection effort, our system obtains high-quality demonstrations that can be used to train fully autonomous task execution policies.
8 pages, 9 figures. Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026
Robotics (cs.RO)
arXiv:2610.25006 [cs.RO]
(or arXiv:2610.25006v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.25006 Focus to learn more arXiv-issued DOI via DataCite
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