Researchers from Google’s DeepMind Robotics team have effectively developed a solidly amateur human-level player when pitted against a human component. During testing, the table tennis bot was able to beat all of the beginner-level players it faced. With intermediate players, the robot won 55% of matches. It’s not ready to take on pros, however. The robot lost every time it faced an advanced player. All told, the system won 45% of the 29 games it played.
The system’s biggest shortcoming is its ability to react to fast balls. DeepMind suggests the key reasons for this are system latency, mandatory resets between shots and a lack of useful data. Other exploitable issues with the system are high and low balls, backhand and the ability to read the spin on an incoming ball. As far as how such research could affect robotics beyond the very limited usefulness of table tennis, its ability to adapt its strategy in real time.
More information:
https://techcrunch.com/2024/08/08/google-deepmind-develops-a-solidly-amateur-table-tennis-robot/