A Probabilistic Planning Framework for Planar Grasping Under Uncertainty

Abstract

How can a robot design a sequence of grasping actions that will succeed despite the presence of bounded state uncertainty and an inherently stochastic system? In this paper, we propose a probabilistic algorithm that generates sequential actions to iteratively reduce uncertainty until object pose is uniquely known (subject to symmetry). The plans assume encoder feedback that gives a geometric partition of the post-grasp configuration space based on contact conditions. An offline planning tree is generated by interleaving computationally tractable open-loop action sequence search and feedback state estimation with particle filtering. To speed up planning, we use learned approximate forward motion models, sensor models, and collision detectors. We demonstrate the efficacy of our algorithm on robotic experiments with over 3000 grasp sequences using different object shapes, pressure distributions, and gripper materials where the uncertainty region is comparable to the size of the object in translation and with no information about orientation.