Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

Abstract:Imitation learning for contact-rich manipulation requires high-quality expert data that is expensive to obtain. This makes learning a sample-efficient policy a key issue. To address this, we propose VISTA, a workspace-level equivariant visuotactile diffusion policy for data-efficient contact-rich imitation learning. VISTA projects visual and tactile observations into spherical tokens, injects tactile contact cues into visual spherical directions through permutation-equivariant spherical fusion, and rotates the fused harmonic representation using the end-effector orientation. The resulting representation conditions an equivariant diffusion policy to predict spatially consistent actions. Extensive experiments in both simulation and real-world robotic settings show that VISTA substantially improves data efficiency over strong visuotactile imitation learning baselines. Project website: this https URL

21 pages, 6 figures. Accepted to the 10th Conference on Robot Learning (CoRL 2026)

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.03333 [cs.RO]

(or arXiv:2610.03333v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.03333 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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