Real-time algorithm for table tennis with a desktop robotic arm

Table tennis with collaborative robots has been a challenge in robotics for decades, due to its unique challenges, especially high-speed movements and real-time ball trajectory predictions for responsive and accurate gameplay. Over the years, several table tennis robots have been developed, showing progressively enhanced abilities for returning balls, hitting specific targets, rallying with collaborative human users, and playing amateur-level games. However, these robotic systems remain costly for individuals, often relying on industrial components, or specialized designs. Emerging AI-integrated personal desktop robotic arms could help bridge the performance gap between affordable personal robotic systems and traditional industrial robots, particularly in terms of dexterity, speed, and precision. Despite this potential, desktop robotic arms have not yet been used for table tennis. However, existing table tennis algorithms require specific adaptations to accommodate the constraints of desktop robots. This paper aims to develop a dedicated algorithm for a collaborative table tennis system using a desktop robotic arm to demonstrate the achievable performance of AI-integrated desktop robots. The proposed system utilizes a 5-degree-of-freedom (DOF) serial robot, integrating advanced algorithms and machine learning models to improve performance. This system enables short collaborative rallies, returning 71.3% of balls overall, improving to 81.4% after fine-tuning system parameters — approaching the best one from the literature (88%) using a 7-DOF industrial robotic arm. This underscores the potential of affordable, AI-integrated desktop robotic arms for high-speed human–robot collaboration. Future works will focus on adapting the algorithm for specialized desktop hardware, expanding desktop robots to other applications, and further enhancing their performance. • The algorithm for robotic table tennis is adapted to the limitations of desktop robotic arms. • Optimized modules from the literature are incorporated. • With these specific adaptations, it is possible to play table tennis using a desktop robotic arm. • AI boosts desktop robotic arm performance to near-industrial levels.