SOURCE-LINKED INTELLIGENCE
PredTac: Learning Contact-Rich Manipulation with Predicted Touch
Contact-rich manipulation benefits from tactile feedback, yet physical tactile sensors introduce hardware, calibration, synchronization, and maintenance costs that complicate policy learning and deployment. We formulate predicted touch as an alternative to measured tactile input and present PredTac, a framework that learns to infer tactile states from causal visual observations and robot states and uses the predicted touch as an explicit interface for policy learning and execution. A tactile predictor is first trained with tactile supervision and then used to provide contact information withou
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T08:19:55.000Z
First collected: 2026-09-20T11:41:07.830Z. This is not the publication date.