Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i.e., objects that are unseen or out-of-domain (OOD), camera and bin settings, etc. In this paper, we present an uncertainty-based approach for online learning of grasp predictions for robotic bin picking. 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 an RL problem that will allow us 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 of grasp performance in comparison to conventional online learning methods which incorporate only naive exploration strategies. Video: https://youtu.be/fPKOrjC2QrU

翻译：现有的抓取预测方法大多基于离线学习，忽略了在新拾取场景（例如未见或域外（OOD）物体、相机和料箱设置等）在线适应过程中的探索性抓取学习。本文提出了一种基于不确定性的在线抓取预测学习方法，用于机器人料箱拾取任务。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见环境设置的适应性能。为此，我们首先将在线抓取学习建模为强化学习（RL）问题，从而同时适应抓取奖励预测和抓取姿态。我们基于贝叶斯不确定性量化和分布集成提出了多种不确定性估计方案。在具有不同难度的真实料箱拾取场景中进行了评估：料箱中的物体具有多种挑战性的物理和感知特性，如半透明或全透明、不规则或弯曲表面。实验结果表明，与仅采用简单探索策略的传统在线学习方法相比，我们的方法在抓取性能上实现了显著提升。视频链接：https://youtu.be/fPKOrjC2QrU