Grasping POMDPs

Abstract— We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under compliant motions. These regions can be treated as states in a partially observable Markov decision process (POMDP), which can be solved to yield optimal control policies under uncertainty. We demonstrate the approach on simple grasping problems, showing that it can construct highly robust, efficiently executable solutions.

• Type: Conference
• Year: 2007
• Where: ICRA
• Authors: Kaijen Hsiao, Leslie Pack Kaelbling, Tomás Lozano-Pérez
• Keywords: ICRA 2007, Optimal Control Policies, Partially Observable Markov Decision Process, Robotics, Simple Grasping Problems