Learning To Grasp Under Uncertainty Using POMDPs

Robust object grasping under uncertainty is an essential capability of service robots. Many existing approaches rely on far-field sensors, such as cameras, to compute a grasp pose and perform open-loop grasp after placing gripper under the pose. This often fails as a result of sensing or environment uncertainty. This paper presents a principled, general and efficient approach to adaptive grasping, using both tactile and visual sensing as feedback. We first model adaptive grasping as a partially observable Markov decision process (POMDP), which handles uncertainty naturally. We solve the POMDP for sampled objects from a set, in order to generate data for learning. Finally, we train a grasp policy, represented as a deep recurrent neural network (RNN), in simulation through imitation learning. By combining model-based POMDP planning and imitation learning, the proposed approach achieves robustness under uncertainty, generalization over many objects, and fast execution. In particular, we show that modeling only a small sample of objects enables us to learn a robust strategy to grasp previously unseen objects of varying shapes and recover from failure over multiple steps. Experiments on the G3DB object dataset in simulation and a smaller object set with a real robot indicate promising results.