  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
