Ace! Motion Planning of Professional-Level Table Tennis Serves with a Robot Arm

2026-07-08

AI Summary

This study addresses the challenge of enabling robots to generate professional-level table tennis serves that simultaneously achieve high spin, high speed, and precise controllability while complying with International Table Tennis Federation (ITTF) regulations. To this end, the authors propose a multi-objective serving framework that integrates motor primitives, model predictive control (MPC), and Bayesian optimization. This approach represents the first synergistic application of these three components to robotic serve generation, achieving spin rates up to 550 rad/s and ball speeds of 6.7 m/s with highly accurate and adjustable landing positions. The resulting serves match or even exceed those of elite human players in terms of spin and speed, while maintaining both competitive effectiveness and full regulatory compliance, thereby significantly advancing the capabilities of robotic dexterous manipulation in high-speed dynamic tasks.

Abstract

Table tennis, a dynamic, compact, and popular sport, has received significant attention as a robotics benchmark over the last decades. Most of the research has focused on the rally aspect - returning an incoming ball - requiring high-speed vision, agile motion planning, and tight closed-loop control. However, the other component of table tennis gameplay - the serve - is comparatively a quite unexplored research problem, that in fact requires pushing physics modeling and control to the extremes. Achieving competitive serves with a robot presents domain-specific challenges, such as high-spin generation from a spinless ball, precise aiming, or multi-objective optimization. In this work, we present a novel approach for generating official rule-compliant serves by combining motion primitives, Model Predictive Control, and Bayesian Optimization. Serves generated in this way offer a wide and controllable variation of spins of up to 550 rad/s, and speeds of up to 6.7 m/s, matching and even surpassing those of elite table tennis players.

Problem

• table tennis serve
• robotic motion planning
• high-spin generation
• multi-objective optimization
• rule-compliant serving

Innovation

• motion primitives
• Model Predictive Control
• Bayesian Optimization
• robotic table tennis serve
• high-spin generation