Dexterous learning of the robot hand using machine learning algorithms for object grasping

Abstract

Abstract Grasping is an essential skill that humans possess and replicating or imitating its functionality has been a significant focus in robotics research. Robotic hands by imitating human grasping behavior, can perform versatile grasping tasks and enhance human-robot interactions. Replication of such ability in robots is a challenging problem. To tackle this challenge, deep learning, and computer vision methods are proposed. Through object recognition and transfer learning, these techniques have made robotic grasping more accurate and robot hands can become more autonomous. The main objective of this paper is to implement and compare deep learning and reinforcement learning (RL) methods for achieving a semi-automatic grasp of different objects. This paper proposes a humanoid 5-DoF robot hand designed specifically for grasping tasks. The robotic hand is fabricated using a 3D printer and its fingers are driven by 5 servo motors. In this direction, a pre-trained Convolutional Neural Network (CNN) structure was used to train the robot hand. Additionally, a 5-finger robot hand is simulated in the MuJoCo environment. The RL agent plans and executes appropriate actions in the simulated hand robot and provides positive or negative rewards based on the Q-learning algorithm. Finally, the performance of methods is evaluated on objects. The results demonstrate the RL method achieved a higher grasp accuracy of 95% compared to the CNN method, which achieved a grasp accuracy of 85%. This indicates that the RL method outperformed the CNN method in terms of grasp accuracy for the robot hand and improved results.