Learning Form Closure Grasping with a Four-Pin Parallel Gripper

Abstract

Being able to stably grasp with generalization is one of the distinguished capabilities for building a generic grasping system for robots. In this work, we propose a stable grasping method for four-pin parallel grippers within a reinforcement learning framework. First, a reinforcement learning problem is constructed on the basis of the improved four-pin gripper. Then, the learning policy and the reward function are constructed in consideration of the knowledge of environmental constraint and form closure. Finally, the effectiveness of the designed grasping method is validated in a simulated environment, and the results demonstrate that a safe and stable grasp can be planned for given 2.5D objects.