Combining Reinforcement Learning and Rule-based Method to Manipulate Objects in Clutter

Abstract

Picking up the clustered objects is always a challenging task in robot research field. And reinforcement learning enables robot to adapt to different tasks through plenty of attempts. To reduce the complexity of strategy learning, we propose a framework for robots to pick up the objects in clutter on table based on deep reinforcement learning and rule-based method. To manipulate the objects on table, we mainly divide the robot actions into two categories: one is pushing that uses the reinforcement learning method, while the other one is grasping that is inferred by image morphological processing. The pushing action can separate the stacking objects, create a robust grasp point for the following grasp. The grasp detect algorithm determines if there is a suitable grasp point. Judging on the result of pushing, the grasp detect algorithm will return a reward for pushing learning. Taking images as input, our framework can keep a high grasp rate with low computational complexity, which makes it achieve clutter clearing quickly.