Robot Learning with Visual Predicted Force
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction from the deformation of a compliant Fin Ray gripper. Our approach trains two models. First, we train a visual force estimator on calibration data and use it to annotate task demonstrations with force estimates. Second, we train an action--force proposal policy on these force-augmented demonstrations to jointly generate candidate robot actions and their associated forces. At test time, we sample candidate actions and the forces they are expected to produce, then execute the action whose predicted force is closest to...
| Authors | Haonan Chen, Feiyang Wu, Yuxiang Ma et al. |
|---|---|
| Primary category | cs.RO |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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