Uncertainty-driven Exploration Strategies for Online Grasp Learning

TL;DR

This paper introduces an uncertainty-driven online learning approach for robotic grasp prediction that adapts to new, unseen environments by using Bayesian and ensemble-based uncertainty estimation, improving grasp success rates.

Contribution

It formulates online grasp learning as a reinforcement learning problem and proposes novel uncertainty estimation schemes for better exploration and adaptation in robotic bin picking.

Findings

Significant improvement in grasp success over traditional methods.

Effective uncertainty estimation enhances online adaptation.

Robust performance across challenging real-world scenarios.

Abstract

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