Trustworthy Robotic Grasping: A Credibility Alignment Framework via Self-Regulation Encoding

Although deep learning models may achieve successful grasps in some instances, they often struggle to accurately reflect the true likelihood of success for a given grasp. In this article, we introduce the trustworthy robotic grasping (TRG) problem, aiming to bridge the gap between predicted grasp probabilities and actual grasp success rates. We propose a novel credibility alignment framework through a two-branch network architecture. This architecture generates an adjusting tensor for nonprobabilistic outputs prior to the activation function of the backbone model, which is able to scale the output proportionally to improve the reliability of the predicted probability. To learn the adjusting tensor, a novel self-regulation encoder has been designed, which can extract 3-D local features of the scene for the local associative regulation of nonprobabilistic outputs. To facilitate research in this area, a new TRG dataset has been created. Experimental results reveal that our method not only significantly reduces the expected grasp error (EGE), maximum grasp error (MGE), and latter half expected grasp error (LH-EGE) by up to 50% compared to the precredibility alignment state, but also enhances the grasp success and declutter rates. Real-world experiments further validate the efficacy of our method.