Considerable effort has also been devoted to developing highly accurate models for forecasting match outcomes (Wunderlich and Memmert, 2021). Tennis is a sport well-suited to predictive modelling, with dense tournament schedules generating extensive historical data. The official ranking systems of the Association of Tennis Professionals (ATP) and Women's Tennis Association (WTA) have been shown to exhibit some predictive power for match outcomes (Clarke and Dyte, 2000; Klaassen and Magnus, 2003), but there are notable limitations: for example, ranking points accumulate over a 52-week period, without decay, which can mask recent changes in player form; while match-specific factors, such as surface type, tournament progression difficulty, and margin of victory in individual matches, are overlooked. Some well-known methods have been applied to tennis and modified to accommodate these factors, such as a Bradley-Terry model with surface-specific adjustments (McHale and Morton, 2011) or Elo rating systems that incorporate margin of victory (Kovalchik, 2020; Angelini et al., 2022). Bookmakers are considered the most accurate predictors of match outcomes (Kovalchik, 2016), with sophisticated models that adjust odds based on betting patterns and proprietary methods. Yet, despite the multi-billion dollar betting industry, one limitation that persists is the poor consideration of intransitivity (van Ours, 2025). Intransitivity is analogous to rock-paper-scissors. In tennis, it occurs where player A tends to defeat B, B defeats C, yet C defeats A, violating the assumption of transitive dominance.

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.

We use the two highest frequency ones which result in 776 label categories. Let us consider two datasets as shown in Figure 1. Let's say that the label Fine-grained mismatch problem: This problem occurs when a parent label ( e.g . We use the CIFAR100 [8] and MS COCO panoptic segmentation [7] datasets for this purpose. The third row has the similar thing, but for Dataset 2. Roughly Dataset 1 have 3x more data as the Dataset 2, with a total of 45k images across both.

Our system enables both humanoid-humanoid (left) and humanoid-human (right) matches, achieving rallies of up to 106 consecutive shots against a human opponent. Abstract -- Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic environments through manipulation. T able tennis exemplifies such a challenge: with ball speeds exceeding 5 m/s, players must perceive, predict, and act within sub-second reaction times, requiring both agility and precision. T o address this, we present a hierarchical framework for humanoid table tennis that integrates a model-based planner for ball trajectory prediction and racket target planning with a reinforcement learning-based whole-body controller . The planner determines striking position, velocity and timing, while the controller generates coordinated arm and leg motions that mimic human strikes and maintain stability and agility across consecutive rallies. Moreover, to encourage natural movements, human motion references are incorporated during training. We validate our system on a general-purpose humanoid robot, achieving up to 106 consecutive shots with a human opponent and sustained exchanges against another humanoid.

With Wimbledon under way, I am going to grasp the opportunity to make a perhaps contentious claim: tennis is the most important sport in the history of video games. Sure, nowadays the big sellers are EA Sports FC, Madden and NBA 2K, but tennis has been foundational to the industry. It was a simple bat-and-ball game, created in 1958 by scientist William Higinbotham at the Brookhaven National Laboratory in Upton, New York, that is widely the considered the first ever video game created purely for entertainment. Tennis for Two ran on an oscilloscope and was designed as a minor diversion for visitors attending the lab's annual open day, but when people started playing, a queue developed that eventually extended out of the front door and around the side of the building. It was the first indication that computer games might turn out to be popular.

Wimbledon gets under way today with line judges scrapped for the first time in the tournament's 148-year history - replaced by AI-powered technology. Some of the sport's biggest stars have descended on south-west London for the showpiece two-week event at the All England Club - including defending singles champions Carlos Alcaraz and Barbora Krejčíková. Britain's hopes rest on Jack Draper, Katie Boulter, Cameron Norrie and Emma Raducanu, who will battle through back injury in an attempt to win her second career Grand Slam. And all eyes are on how this year's occasion copes with a shift in the way the game is umpired, as human line judges are replaced by artificial intelligence systems instead. The controversial decision has left fans torn, with some praising the forward-thinking idea while others disliking the idea of technology taking the place of a person.

The moment tennis fans have been waiting for is nearly here – the start of Wimbledon 2025. From Monday, some of the biggest stars will battle for the most prestigious prize in tennis, including defending champions Carlos Alcaraz and Barbora Krejčíková. Britain's hopes rest on Jack Draper, Katie Boulter, Cameron Norrie and Emma Raducanu, who will battle through back injury in an attempt to win her second career Grand Slam. Novak Djokovic aims to win his eighth Wimbledon men's single's title, matching the record set by Roger Federer, but Australian fan favourite Nick Kyrgios will be absent. This year, Wimbledon will do away with human line judges for the first time in its 148-year history, to be replaced with AI.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Erin Routliffe may be the only person unhappy that robots haven't taken over the world. The tennis pro from New Zealand and her partner, Gabriela Dabrowski of Canada, were competing in the third round of women's doubles at the Australian Open Sunday when Routliffe exploded into a brief tirade after she believed her opponent's serve skimmed the net. Erin Routliffe of New Zealand and Gabriela Dabrowski of Canada in action against Laura Siegemund of Germany and Beatriz Haddad Maia of Brazil in the third round of women's doubles at the 2025 Australian Open at Melbourne Park Jan. 20, 2025, in Melbourne, Australia. The contentious point came during a tiebreak in the first set with Beatriz Haddad Maia of Brazil serving.

In this paper, we propose a novel and generalizable zero-shot knowledge transfer framework that distills expert sports navigation strategies from web videos into robotic systems with adversarial constraints and out-of-distribution image trajectories. Our pipeline enables diffusion-based imitation learning by reconstructing the full 3D task space from multiple partial views, warping it into 2D image space, closing the planning loop within this 2D space, and transfer constrained motion of interest back to task space. Additionally, we demonstrate that the learned policy can serve as a local planner in conjunction with position control. We apply this framework in the wheelchair tennis navigation problem to guide the wheelchair into the ball-hitting region. Our pipeline achieves a navigation success rate of 97.67% in reaching real-world recorded tennis ball trajectories with a physical robot wheelchair, and achieve a success rate of 68.49% in a real-world, real-time experiment on a full-sized tennis court.