AI RESEARCH
Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
arXiv CS.CV
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ArXi:2512.23864v3 Announce Type: replace-cross Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain blind to physical contact. Consequently, they struggle with contact-rich manipulation tasks that require reasoning about force, texture, and slip. While some approaches incorporate low-dimensional tactile signals, they fail to capture the high-resolution dynamics essential for such interactions. To address this limitation, we.