Grasping Under Uncertainties: Sequential Neural Ratio Estimation for 6-DoF Robotic Grasping

Abstract

We introduce a novel approach to 6-DoF robotic grasping based on simulation-based inference. Our approach combines sequential neural ratio estimation with a neural implicit representation for the Bayesian inference of hand configurations in cluttered environments. We propose to compute the maximum a posteriori by gradient descent, more specifically using Riemannian gradient descent, to preserve the geometry of the rotation space and capitalize on the full differentiability of our model. We demonstrate the capabilities of our approach on a grasping benchmark both in simulation and on a real robot. Our performance generalizes well across different scenarios, achieving high success rates.