Jonas Tebbe, Yapeng Gao
The chair is equipped with a KUKA Agilus Robot with 6 degrees of freedom. Motivated by the KUKA comercial (https://www.youtube.com/watch?v=tIIJME8-au8) we are teaching the robot to play table tennis.
A high-speed vision system was set up to identify a table tennis ball in the scene and infer its 3D position. To find the ball in an image we use three filters which take the balls movement, color, and shape into account. Its 3D position is estimated by triangulation using the balls pixel coordinates in both camera frames. To speedup the image processing, we restrict to a small region of interest if a ball is already found in the previous frame. Based on the position information, trajectory state is estimated by an extended Kalman filter and predicted into the future using a airodynamic force model. A description of the robot system is contained in .
A sample-efficient RL algorithm was developed to learn the parameters for a successful robotic return stroke . Every table tennis stroke is different, with varying placement, speed and spin. But to returning the ball it is only important what the state of the ball (position, velocity, spin) and racket (pose, velocity) is at hitting time. Therefore, we decided to have an reinforcement learning algorithm use our prediction for the ball state to suggesting the racket hitting state. Then we use a path planner (Reflexxes Library) to generate a fitting trajectory for that state.
An actor-critic based deterministic policy gradient algorithm was developed for accelerated learning. Our approach achieves accurate return on the real robot in a number of challenging scenarios within 200 balls of training. The video presenting our experiments is shown below.
We also uploaded a video with a complete training process (only 7 minutes) for second scenario (I-play). The corresponding results is found in Figure 3.
|||Jonas Tebbe, Lukas Krauch, Yapeng Gao, and Andreas Zell. Sample-efficient Reinforcement Learning in Robotic Table Tennis. In 2021 IEEE International Conference on Robotics and Automation (ICRA), Xian, China, May 2021. (Accepted for publication). [ link ]|
|||Jonas Tebbe, Lukas Klamt, Yapeng Gao, and Andreas Zell. Spin Detection in Robotic Table Tennis. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 9694--9700, Paris, France, May 2020. [ DOI | link ]|
|||Yapeng Gao, Jonas Tebbe, Julian Krismer, and Andreas Zell. Markerless Racket Pose Detection and Stroke Classification based on Stereo Vision for Table Tennis Robots. In 2019 Third IEEE International Conference on Robotic Computing (IRC), pages 189--196, Naples, Italy, February 2019.|
|||Jonas Tebbe, Yapeng Gao, Marc Sastre-Rienitz, and Andreas Zell. A Table Tennis Robot System using an industrial KUKA Robot Arm. In German Conference on Pattern Recognition (GCPR), Stuttgart, Germany, October 2018. [ DOI ]|