Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping

Robotic grasping in highly noisy environments presents complex challenges,
especially with limited prior knowledge about the scene. In particular,
identifying good grasping poses with Bayesian inference becomes difficult due
to two reasons: i) generating data from uninformative priors proves to be
inefficient, and ii) the posterior often entails a complex distribution defined
on a Riemannian manifold. In this study, we explore the use of implicit
representations to construct scene-dependent priors, thereby enabling the
application of efficient simulation-based Bayesian inference algorithms for
determining successful grasp poses in unstructured environments. Results from
both simulation and physical benchmarks showcase the high success rate and
promising potential of this approach.