A Probabilistic Planning Framework for Planar Grasping Under Uncertainty.

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 letter, 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 part...