This weekend saw the finale of the league competitions at RoboCup 2026 in Incheon, South Korea, with the winners in the small, middle, and large humanoid divisions decided. You can watch the action from one of the semi-finals in the middle division, which saw HTWK take on Rhoban. Although the competitions have drawn to a close, RoboCup 2026 continues today with a symposium, which brings together researchers and practitioners from around the world to present and discuss innovative research in robotics and artificial intelligence. You can find out more here . Lucy Smith is Senior Managing Editor for Robohub and AIhub.

The second day's play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia. In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use.

Claire chatted to Allison Okamura from Stanford University about developing advanced robotic systems for haptic (touch) interaction. Allison Okamura is the Richard W. Weiland Professor of Engineering at Stanford University. Her academic interests include haptics, teleoperation, virtual reality, medical robotics, soft robotics, rehabilitation, and education. Allison is Director of Graduate Studies for Mechanical Engineering at Stanford University, a deputy director of the Wu Tsai Stanford Neurosciences Institute, a Science Fellow of the Hoover Institution and a founding faculty member and executive committee member of the Stanford Robotics Center. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Since the first robot-assisted surgery was performed, over 40 years ago, major advances in robotics, computer vision and artificial intelligence have fundamentally changed medicine and healthcare. Innovative new technologies are already aiding skilled medical professionals in diagnosis, surgery, rehabilitation and beyond. But many questions remain: What ethical issues arise as medical tools become increasingly autonomous? How do we regulate technologies that can learn and change over time? And how can we ensure that cutting-edge medical devices are accessible to all?

When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and inverse problems difficult, since both machine learning algorithms and probabilistic samplers rely on the distinguishability of data and its gradients with respect to parameters. However, identifying degeneracies in physical models or real-world datasets can be elucidating about the choice of model or the underlying process that produces the data. We present the degeneracy distillery, a method that (1) detects and (2) resolves degenerate parameter combinations (a) automatically and (b) symbolically, from parameter-data (or parameter-simulation) pairs alone, through estimation and flattening of the Fisher information matrix. By exploring the information geometry of the likelihood, we characterize degeneracies as an intrinsic property of the physical model, requiring no realised data observation. We demonstrate our approach on a range of synthetic and real-world problems, discovering symbolic coordinate transformations that identify the combinations of parameters of a model which yield independent effects on the data. The resulting coordinates flatten the Fisher information in expectation globally, in contrast to posterior-based methods that flatten only at a single point, and substantially reduce the simulation budget required for downstream neural posterior estimation. In test cases we require up to $10\times$ fewer simulations for posterior estimation at matched validation calibration whilst simultaneously gaining physical insight on the system.

The AAMAS 2026 best paper awards were presented at the 25th International Conference on Autonomous Agents and Multiagent Systems, which took place from 25-29 May 2025 in Paphos, Cyprus. Lucy Smith is Senior Managing Editor for Robohub and AIhub. Lucy Smith is Senior Managing Editor for Robohub and AIhub. In this special live recording at the Great Exhibition Road Festival in London, Claire chatted to George Mylonas (Imperial College London), Antonia Tzemanaki (University of Bristol) and Tom Vercauteren (King's College London) about robotics and AI in medicine and healthcare. Researchers are developing AI models that could one day enable vision prosthetics able to restore meaningful, object-level sight for the blind.

King's College team wins access to cutting-edge Google quantum chip Scientists from King's College London have become the first UK academic research team to gain access to Google's cutting-edge quantum computer chip Willow as part of a scheme launched with the UK's national quantum lab last year. Quantum computers can in theory solve problems which the most powerful conventional computers cannot. King's lead for the project Dr Eleanor Crane said its use of Willow would light a torch for research to answer questions about the most important natural processes. It would be useful if society could understand how plants transform sunlight into energy, find materials which transport electricity quickly, or how molecules bind to each other, said Crane, who will co-lead the research team alongside Dr Alexander Schuckert from ENS Paris. These natural processes rely on the interactions between many fundamental particles which made up the building blocks of life.

AI Is Getting Better at Science. Demis Hassabis founded DeepMind to "solve intelligence" and then use that to "solve everything else." Sam Altman promised that "the gains to quality of life from AI driving faster scientific progress will be enormous." Dario Amodei of Anthropic predicted that as soon as 2026, AI progress could produce a "country of geniuses in a data center." Of all the foundational myths driving the AI boom, the hope that AI might help humanity understand the universe is among the most enduring. FrontierScience, a new benchmark published Tuesday by OpenAI, suggests that AI models are advancing toward that goal--and highlights the difficulty of testing models' capabilities as they become ever more competitive with human scientists.

Abstract--Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference. Driven by the continued growth of computing resources and training datasets, artificial intelligence (AI) research is widely considered to be in the scaling era, which is focused on the development of general-purpose models that exhibit emergent capabilities. While this trend has yielded impressive results for many tasks, particularly in the domain of language modeling, it poses unique challenges when applied to engineering domains such as telecommunication networks.

Health Medicine Stinky'rotten egg' gas could fight nail infections Don't worry, scientists are working on the odor. Breakthroughs, discoveries, and DIY tips sent every weekday. If you have ever let a container of hardboiled eggs spoil or visited a volcano that is spewing lava and gas, you've likely taken a whiff of hydrogen sulfide. This colorless and flammable gas has a uniquely unpleasant rotten egg smell. However that nasty smell (and the gas it belongs to) could have a new use treating pesky infections.