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Uncertainty-aware deep learning for robot touch: Application to Bayesian tactile servo control
[article]
2021
arXiv
pre-print
This work investigates uncertainty-aware deep learning (DL) in tactile robotics based on a general framework introduced recently for robot vision. For a test scenario, we consider optical tactile sensing in combination with DL to estimate the edge pose as a feedback signal to servo around various 2D test objects. We demonstrate that uncertainty-aware DL can improve the pose estimation over deterministic DL methods. The system estimates the uncertainty associated with each prediction, which is
arXiv:2104.14184v1
fatcat:quyqdwlwqfcitmbksdzmhzsqaq