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 15.4ms−1and 192.0s−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, and 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.

Versatile motor skills for hitting and throwing motions can be observed in humans already in early ages. Future robots require high power-to-weight ratios as well as inherent long operational lifetimes without breakage in order to achieve similar perfection. Robustness due to passive compliance and high-speed catapult-like motions as possible with fast energy release are further beneficial characteristics. Such properties can be realized with antagonistic muscle-based designs. Additionally, control algorithms need to exploit the full potential of the robot. Learning control is a promising direction due to its the potential to capture uncertainty and control of complex systems. The aim of this paper is to build a robotic arm that is capable of generating high accelerations and sophisticated trajectories as well as enable exploration at such speeds for robot learning approaches. Hence, we have designed a light-weight robot arm with moving masses below 700 g with powerful antagonistic compliant actuation with pneumatic artificial muscles. Rather than recreating human anatomy, our system is designed to be easy to control in order to facilitate future learning of fast trajectory tracking control. The resulting robot is precise at low speeds using a simple PID controller while reaching high velocities of up to 12 m/s in task space and 1500 deg/s in joint space. This arm will enable new applications in fast changing and uncertain task like robot table tennis while being a sophisticated and reproducible test-bed for robot skill learning methods. Construction details are available.