Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and predict trajectories, and a novel model predictive control (MPC) formulation for agile full-body control. Notably, a continuous set of stroke strategies emerge automatically from different ball return objectives using this control paradigm. We demonstrate our system in the real world on a Spot quadruped, evaluate accuracy of each system component, and exhibit coordination through the system's ability to aim and return balls with varying spin types. As a further demonstration, the system is able to rally with human players.

The game of table tennis is renowned for its extremely high spin rate, but most table tennis robots today struggle to handle balls with such rapid spin. To address this issue, we have contributed a series of methods, including: 1. Curriculum Reinforcement Learning (RL): This method helps the table tennis robot learn to play table tennis progressively from easy to difficult tasks. 2. Analysis of Spinning Table Tennis Ball Collisions: We have conducted a physics-based analysis to generate more realistic trajectories of spinning table tennis balls after collision. 3. Definition of Trajectory States: The definition of trajectory states aids in setting up the reward function. 4. Selection of Valid Rally Trajectories: We have introduced a valid rally trajectory selection scheme to ensure that the robot's training is not influenced by abnormal trajectories. 5. Reality-to-Simulation (Real2Sim) Transfer: This scheme is employed to validate the trained robot's ability to handle spinning balls in real-world scenarios. With Real2Sim, the deployment costs for robotic reinforcement learning can be further reduced. Moreover, the trajectory-state-based reward function is not limited to table tennis robots; it can be generalized to a wide range of cyclical tasks. To validate our robot's ability to handle spinning balls, the Real2Sim experiments were conducted. For the specific video link of the experiment, please refer to the supplementary materials.

Table tennis robots gained traction over the last years and have become a popular research challenge for control and perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. So far, most table tennis robots use conventional, frame-based cameras for the perception pipeline. However, frame-based cameras suffer from motion blur if the frame rate is not high enough for fast-moving objects. Event-based cameras, on the other hand, do not have this drawback since pixels report changes in intensity asynchronously and independently, leading to an event stream with a temporal resolution on the order of us. To the best of our knowledge, we present the first real-time perception pipeline for a table tennis robot that uses only event-based cameras. We show that compared to a frame-based pipeline, event-based perception pipelines have an update rate which is an order of magnitude higher. This is beneficial for the estimation and prediction of the ball's position, velocity, and spin, resulting in lower mean errors and uncertainties. These improvements are an advantage for the robot control, which has to be fast, given the short time a table tennis ball is flying until the robot has to hit back.

The first sports robot which Tanaka created, was one that reproduced particular techniques found in Japanese martial arts. Traditional Japanese martial arts feature a variety of body control exercises, within which exist some truly amazing techniques. One such technique allows you to "defeat your opponent without having to use any force at all." During his time studying for his postgraduate degree, Tanaka met Kunihiro Ogata - a senior member of the Intelligent Systems and Informatics Laboratory (currently at the National Institute of Advanced Industrial Science and Technology). After studying these martial arts, Ogata decided to incorporate motion-capture technology in an attempt to measure and scientifically analyze the body control exercises found in martial arts.The aim was to see if he could gain some clear understanding of the principles behind these techniques and discover if they could be incorporated into the design and manipulation of robots.

Omron's table tennis robot is getting smarter Forpheus, the mighty table tennis robot developed by Japan's Omron, is getting smarter. An updated version on show at the Ceatec electronics show this week has artificial intelligence to become a tougher opponent. In the new version, the robot attempts to rank a player according to their perceived skill as a beginner, intermediate player or advanced. It does this by looking at the speed of the served ball, its trajectory, rotation and the body motion of the player with cameras, and does so with 90 percent accuracy, according to Omron. The machine uses that information to customize its return ball, softer and easy for beginners, faster and more unpredictable for advanced players. The use of artificial intelligence has also improved the robot's game.