Demo Data from SIGIR 2026 / reviewers in the wild / expert
Thomas Steinbrenner
dblp:302/3653
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot manipulation · 62% Motion planning and robot control · 19% Reinforcement learning · 19% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › nonprehensile manipulation › dynamic manipulation
table tennis robot |
0.7 | 1 | 2023 | AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation · ICRA 2023 |
Machine learning › Reinforcement learning
real-world reinforcement learning |
0.2 | 1 | 2023 | AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation · ICRA 2023 |
Robotics › Motion planning and robot control
robot learning |
0.2 | 1 | 2023 | AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
trajectory learning · 0.7open-source hardware design · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory GenerationabstractTo 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. Alexander Dittrich, Jan Schneider 0007, Simon Guist, Nico Gürtler, Heiko Ott, Thomas Steinbrenner, Bernhard Schölkopf, Dieter Büchler |
ICRA | 6 |