Jonas Tebbe

About

Jonas Tebbe is a robotics and computer vision researcher whose work centers on the demanding domain of robotic table tennis — a field that combines high-speed perception, real-time control, and machine learning in one of robotics' most challenging environments. His most cited work, "A Table Tennis Robot System Using an Industrial KUKA Robot Arm" (2019, 49 citations), established a foundational platform for subsequent research, demonstrating how industrial robotic arms can be adapted for dynamic, high-speed gameplay. Building on this, Tebbe has made significant contributions to stroke recognition and racket pose estimation using stereo vision and IMU sensors, enabling robots to interpret and respond to an opponent's movements in real time. His work on spin estimation through SpinDOE addresses one of table tennis robotics' most elusive challenges — accurately measuring ball spin at high velocities. Tebbe has also advanced reinforcement and policy gradient learning approaches to help robots acquire and refine strokes with limited training samples, a critical constraint in physical robotic systems. More recently, his exploration of neuromorphic hardware and event camera simulation signals a broadening research vision toward energy-efficient, ultra-fast sensory processing. With over 130 cumulative citations, Tebbe represents an emerging voice in intelligent robotic sports systems.

Research Focus

Key Achievements

Top Papers

1. 1
   A Table Tennis Robot System Using an Industrial KUKA Robot Arm

   49 citations · 2019
2. 2
   Optimal stroke learning with policy gradient approach for robotic table tennis

   20 citations · 2022
3. 3
   Markerless Racket Pose Detection and Stroke Classification Based on Stereo Vision for Table Tennis Robots

   15 citations · 2019
4. 4
   SpinDOE: A Ball Spin Estimation Method for Table Tennis Robot

   14 citations · 2023
5. 5
   Robust Stroke Recognition via Vision and IMU in Robotic Table Tennis

   10 citations · 2021
6. 6
   A Model-free Approach to Stroke Learning for Robotic Table Tennis

   9 citations · 2022
7. 7
   Real-time event simulation with frame-based cameras

   8 citations · 2023
8. 8
   Sample-efficient Reinforcement Learning in Robotic Table Tennis

   5 citations · 2021
9. 9
   Detection of Fast-Moving Objects with Neuromorphic Hardware

   2 citations · 2025

Key Collaborators

Contact & Links