Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation
Quick summary
arXiv:2610.03333v1 Announce Type: cross 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 usin
Key takeaways
- arXiv:2610.03333v1 Announce Type: cross 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation” may reshape data collection, model training, output accountability and market access.
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