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Robot Learning with Visual Predicted Force: Achieving Force-Aware Manipulation Through Visual Force Prediction

The paper proposes achieving force-aware manipulation by visually predicting the deformation of a compliant Fin Ray gripper, without requiring force or tactile sensors at deployment. The method trains two models: first, a visual force estimator is trained on calibration data to annotate demonstrations with force estimates; second, an action-force proposal policy is trained to jointly generate candidate actions and their corresponding forces. At test time, candidate actions are sampled and the action whose predicted force is closest to a target from the demonstrations is executed. The method was evaluated on four tasks: berry picking, empty-can grasping, in-hand reorientation, and plug insertion.

Part of the event: Visual Predictive Power Enables Sensorless Force-Aware Manipulation

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