SOURCE-LINKED INTELLIGENCE
HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing
Contact-rich manipulation requires estimating forces, slip and contact geometry that can remain ambiguous in scene images. Optical tactile sensors provide both visual observations of the contact surface and mechanical measurements, yet learning from these signals raises two challenges: representing contact beyond appearance and transferring its benefits to a policy that does not require fingertip observations at deployment. We introduce HapticWAM, a world-action model that combines heterogeneous tactile encoding, structured contact prediction and teacher-student distillation. Its teacher encod
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-20T21:49:50.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.