Vision-Force Admittance Learning for Peg Insertion into a Movable Hole

Abstract:Precise manipulation in dynamic environments, whether induced by a mobile robot base or a target with unknown motion, remains a major challenge in robotics. Manipulation in dynamic environments introduces substantial uncertainty, which fundamentally conflicts with the tight precision requirement of precise tasks such as peg-in-the-hole. We propose a Vision-Force Admittance Learning (VFAL) framework that fuses asynchronous visual feedback with a high-frequency force-based model, using visual pose estimations as a regularization term. VFAL adapts insertion strategies online to dynamic motion while maintaining millimeter-level precision. To obtain robust, low-frequency pose information, we employ state-of-the-art vision foundation models for visual pose estimation. Additionally, we incorporate failure recovery mechanisms to enhance overall robustness. We validate our approach in real-world experiments, demonstrating high success rates and strong adaptability to various pegs and dynamic environments.

8 pages, 5 figures, 2 tables. Accepted to IEEE Robotics and Automation Letters (RA-L). Project page: this https URL

Robotics (cs.RO)

arXiv:2609.14133 [cs.RO]

(or arXiv:2609.14133v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.14133 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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