Learning Collaborative Pushing and Grasping Policies in Dense Clutter

Robots must reason about pushing and grasping in order to engage in flexible manipulation in cluttered environments. Earlier works on learning pushing and grasping only consider each operation in isolation or are limited to top-down grasping and bin-picking. We train a robot to learn joint planar pushing and 6-degree-of-freedom (6-DoF) grasping policies by self-supervision. Two separate deep neural networks are trained to map from 3D visual observations to actions with a Q-learning framework. With collaborative pushes and expanded grasping action space, our system can deal with cluttered scenes with a wide variety of objects (e.g. grasping a plate from the side after pushing away surrounding obstacles). We compare our system to the state-of-the-art baseline model VPG [1] in simulation and outperform it with 10% higher action efficiency and 20% higher grasp success rate. We then demonstrate our system on a KUKA LBR iiwa arm with a Robotiq 3-finger gripper.