CompVLA: A Variable Compliance Vision-Language-Action Model for Contact-rich Manipulation

Abstract:Contact-rich manipulation, requiring robots to regulate not only motion but also how they yield to external forces, has emerged as the next frontier for Vision-Language-Action (VLA) models. However, existing VLAs output purely kinematic commands, degrading performance on real-world contact-rich tasks. In this paper, we introduce CompVLA, a unified VLA framework that jointly predicts motion and stiffness matrix from RGB and language inputs. Our approach augments the conventional architecture with a dedicated Compliance Expert, which outputs time-varying stiffness and virtual displacement profiles executed via geometric impedance control. We demonstrate that CompVLA achieves the highest average success rate across diverse contact-rich tasks, outperforming both vanilla and compliance-aware VLA baselines, with ablations confirming each component is essential.

Robotics (cs.RO)

arXiv:2609.23614 [cs.RO]

(or arXiv:2609.23614v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.23614 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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