Hardware Design for Table Tennis Robot Capable of Beating Professional Players

2026-06-25

AI Summary

This work proposes a custom 8-degree-of-freedom robotic system, “Ace,” designed to defeat professional table tennis players. By analyzing elite human motions, the study establishes key specifications—including workspace, payload capacity, disturbance rejection, serving capability, and end-effector precision—and employs topology optimization to achieve a lightweight yet highly rigid structure. Motor and reducer selection is refined through inverse dynamics modeling, while a low-order joint dynamics model with delay compensation is integrated to enable reinforcement learning–based control. The resulting system executes full-stroke swings within a 0.8-second cycle, achieving a racket-tip peak velocity of 22 m/s, and has successfully defeated multiple professional players, thereby validating the efficacy of high-bandwidth servo actuation combined with data-driven control in high-speed human–robot competition.

Abstract

This paper focuses on the hardware specifications required for a table tennis robot to beat professional players. After analyzing the motions of elite players, we defined target specifications for the workspace, payload, external-force resistance, physical performance, serve capability, and end-effector accuracy. Based on these specifications, we developed "Ace", a custom 8-DoF robot. The mechanical structure was improved through topology optimization to minimize mass while preserving stiffness. Motor and gearbox selection was optimized using an inverse-dynamics torque model. Low-order per-joint dynamics models with delay compensation were identified and integrated into simulation to enable the use of an RL control policy. Experiments demonstrated repeated full-stroke swings with a cycle time of 0.8 s and a peak racket-center velocity of 22 m/s. The robot successfully defeated multiple professional players.

Problem

• table tennis robot
• hardware design
• professional players
• robot performance
• mechanical specifications

Innovation

• topology optimization
• inverse-dynamics torque model
• delay-compensated dynamics
• reinforcement learning control
• high-speed robotic table tennis