Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
This work addresses robotic grasping challenges for robots in noisy environments, representing an incremental improvement by combining implicit priors with Riemannian geometry for Bayesian methods.
The paper tackled the problem of robotic grasping in noisy, unstructured environments by using implicit representations to construct scene-dependent priors, enabling efficient Bayesian inference for grasp poses, with results showing a high success rate in simulations and physical benchmarks.
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.