The method for manipulator grasping based on tactile sensor and reinforcement learning intrinsic reward

Abstract:

Although play an important role in the process of the robot grasping, haptics is only used to extend the state space, and the information provided by it such as position and pressure is rarely fully utilized in most reinforcement tasks. In order to solve the issues, inspired by the intrinsic reward mechanism, an intrinsic incentive method based on the “inverted T” array sensor is proposed. According to the position where the end effector of the robot touches the object, the method gives degrees of importance, and encourages the agent to achieve the goal with a more effective posture. Finally, the method was tested in the simulation environment, and the results showed that the speed of convergence of the method in the task gripping ellipsoid objects was about 20% faster than the latest benchmark method.