AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation
Not on the map. Why?
Abstract (arXiv)
To approach the level of advanced human players in table tennis with robots, generating varied ball trajectories in a reproducible and controlled manner is essential. Current ball launchers used in robot table tennis either do not provide an interface for automatic control or are limited in their capabilities to adapt speed, direction, and spin of the ball. For these reasons, we present AIMY, a three-wheeled open-hardware and open-source table tennis ball launcher, which can generate ball speeds and spins of up to 15.4 ms-1 and 192 s-1, respectively, which are comparable to advanced human players. The wheel speeds, launch orientation and time can be fully controlled via an open Ethernet or Wi-Fi interface. We provide a detailed overview of the core design features, as well as open source the software to encourage distribution and duplication within and beyond the robot table tennis research community. We also extensively evaluate the ball launcher's accuracy for different system settings and learn to launch a ball to desired locations. With this ball launcher, we enable long-duration training of robot table tennis approaches where the complexity of the ball trajectory can be automatically adjusted, enabling large-scale real-world online reinforcement learning for table tennis robots.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Oct 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.
3 papers.
- 2023Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impactscited by 4
- 2026TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videoscited by 1
- 2026Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networkscited by 0
- 2026TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos2 May 2026
- 2026Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks10 Apr 2026
- 2023Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts24 May 2023