Reinforcement Learning for Dynamic Task Execution: A Robotic Arm for Playing Table Tennis

Dynamic tasks like table tennis pose a significant challenge for robots, although relatively easy for humans to learn. These tasks require precise control of rapid movements and timing in situations where the estimation of the flying ball and the robot's state is imprecise. Traditional model-based control methods struggle to accurately model and adapt to these dynamic changes, leading to difficulties and challenges in practical applications. To address this issue, a table tennis hitting method for a robotic arm based on reinforcement learning (RL) is proposed in this paper. Firstly, a simulation model of a robotic arm with eight degrees of freedom for playing table tennis was constructed. Secondly, a simple and effective reward function was designed to accomplish the task of hitting the table tennis ball with the robotic arm. Finally, two sets of experiments involving single and random ball launches were designed. Experimental results demonstrate that the proposed method enables the robotic arm to accurately hit the table tennis ball, showcasing the potential of RL in dynamic tasks.