Existing grasp prediction approaches are mostly based on offline learning, while, ignored the exploratory grasp learning during online adaptation to new picking scenarios, i.e., unseen object portfolio, camera and bin settings etc. In this paper, we present a novel method for online learning of grasp predictions for robotic bin picking in a principled way. Existing grasp prediction approaches are mostly based on offline learning, while, ignored the exploratory grasp learning during online adaptation to new picking scenarios, i.e., unseen object portfolio, camera and bin settings etc. In this paper, we present a novel method for online learning of grasp predictions for robotic bin picking in a principled way. Specifically, the online learning algorithm with an effective exploration strategy can significantly improve its adaptation performance to unseen environment settings. To this end, we first propose to formulate online grasp learning as a RL problem that will allow to adapt both grasp reward prediction and grasp poses. We propose various uncertainty estimation schemes based on Bayesian Uncertainty Quantification and Distributional Ensembles. We carry out evaluations on real-world bin picking scenes of varying difficulty. The objects in the bin have various challenging physical and perceptual characteristics that can be characterized by semi- or total transparency, and irregular or curved surfaces. The results of our experiments demonstrate a notable improvement in the suggested approach compared to conventional online learning methods which incorporate only naive exploration strategies.

翻译：现有抓取预测方法大多基于离线学习，忽略了在线适应新拾取场景（例如未知物体组合、相机与料箱设置等）过程中的探索性抓取学习。本文提出了一种以原则性方式实现机器人料箱抓取在线学习的新型方法。具体而言，采用有效探索策略的在线学习算法能显著提升其对未知环境设置的适应性能。为此，我们首先将在线抓取学习构建为强化学习问题，以同时适应抓取奖励预测与抓取姿态。基于贝叶斯不确定性量化与分布式集成，我们提出了多种不确定性估计方案。我们在不同难度的真实料箱拾取场景中进行了评估，料箱内物体具有多样的挑战性物理与感知特性（如半透明或全透明、不规则或曲面外形）。实验结果表明，与仅采用朴素探索策略的传统在线学习方法相比，本文方法取得了显著性能提升。