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This research demonstrates that robots can achieve force-aware manipulation for delicate tasks like berry picking by predicting contact forces through visual analysis of gripper deformation, eliminating the need for dedicated tactile sensors at deployment. By training separate models to estimate force from camera data and then selecting actions based on these predictions, the approach enables robust contact-rich interaction using only standard vision systems. This method bridges a critical gap in robot hardware requirements while advancing the capabilities of general-purpose AI agents in physical environments.
Robot Learning with Visual Predicted Force
arXiv:2610.04741v1 Announce Type: cross Abstract: 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 a target from the demonstrations. We evaluate our approach on berry picking, empty-can grasping, in-hand reorientation, and plug insertion. Our results show that visual force prediction can guide inference-time action selection for contact-rich manipulation without requiring force or tactile sensors at deployment.
Authors:Haonan Chen, Feiyang Wu, Yuxiang Ma, Mustafa Mete, Pengfei Ye, Junxuan Shen, Cheng Zhu, Aurora Ruggeri, Kelvin Cheung, Jiayuan Mao, Edward Adelson, Jiajun Wu, Robert D. Howe, Yilun Du
Abstract: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 a target from the demonstrations. We evaluate our approach on berry picking, empty-can grasping, in-hand reorientation, and plug insertion. Our results show that visual force prediction can guide inference-time action selection for contact-rich manipulation without requiring force or tactile sensors at deployment.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.04741 [cs.RO]
(or arXiv:2610.04741v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.04741 arXiv-issued DOI via DataCite (pending registration)
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