Towards simplicity and practicality:
A novel framework and guidance for robotic table tennis applications

Although many impressive advances have been reported in table tennis robots field using the reinforcement learning method, challenges related to policy complexity and adaptability continue to hinder large-scale deployment and practical application. In this work, building upon extensive prior studies, we propose several techniques and integrate them into a unified framework that reduces the difficulty of training and deployment while enhancing the human player enjoyment. Specifically, a recursively nested design is introduced in the hierarchical decision-making system, which fully separates high-level decision-making from low-level execution, eliminating the need to train multiple workers at the execution layer. The multi-objective problem is also studied by introducing a tolerance-based weight modulation mechanism, which can balance the landing accuracy with other objectives and adjust the playing strategy to meet diverse goals. Comprehensive experiments demonstrate the effectiveness of the proposed framework and techniques. Notably, the robot achieved up to 18 consecutive rallies with human player, which, to the best of our knowledge, is the longest rally attained with a fixed-base collaborative robot arm. The proposed methods can be readily extended to other racket-sport robots or similar tasks.