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arXiv AI By Lik Hang Kenny Wong, Yiyao Ma, Xiu-Shen Wei, Zelong Tan, Zhuheng Song, Dongsheng Xie, Kai Chen, Qi Dou

Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

Read the original on arXiv AI →

The paper introduces VISTA, a workspace-level equivariant visuotactile diffusion policy designed for data-efficient imitation learning in contact-rich manipulation tasks. VISTA transforms visual and tactile inputs into spherical tokens, fuses them through permutation-equivariant spherical fusion, and aligns the fused representation with the end-effector orientation to produce spatially consistent actions. Experiments in simulation and real-world robotics demonstrate that VISTA significantly improves data efficiency compared to strong visuotactile imitation learning baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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