Probabilistic sensor-based grasping

Abstract

In this paper, we present a novel probabilistic framework for grasping. In the framework, grasp and object attributes, on-line sensor information and the stability of a grasp are all considered through probabilistic models. We describe how sensor-based grasp planning can be formulated in a probabilistic framework and how information about object attributes can be updated simultaneously using on-line sensor information gained during grasping. The framework is demonstrated by building the necessary probabilistic models using Gaussian process regression, and using the models with an MCMC approach to estimate a target object's pose and grasp stability during grasp attempts. The framework is also demonstrated on a real robotic platform.