Robot foundation models look great in simulation, but fail the moment the camera angle or lighting changes. Turns out they might just be cheating on looks. Title: Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models URL: 📝 Overview This paper proposes Latent Interface Training (LIT), a method that breaks the "vision-action shortcut" where models exploit task-irrelevant visual cues that just happened to correlate with actions in the training distribution. ❗ Problem it solves Robot foundation models perform well in-distribution but degrade sharply under visual distribution shift — different camera viewpoints, lighting, or sensor noise. ⚙️ Methodology It's a two-stage recipe: first pretrain the action expert using only pose goals, no images at all, then route all visual information through 100 latent tokens supervised with a pose-reconstruction loss. 🤖 Use cases It applies to 4 different robot foundation models — π0.5, MolmoAct2, FAST-WAM, and ImageWAM — making it a genuinely framework-agnostic technique. 📊 Results On LIBERO-Plus out-of-distribution evaluation, every model improved by 3.87 to 10.70 points. In real-world robot manipulation, it gained +16.7 points under lighting changes, and up to +60 points on a specific task. Relying on real spatial information instead of surface appearance feels like a direct path to more trustworthy robots in the field. #Robotics# #VLA#

Robot actions can be represented as motion in pixel space. NVIDIA Research introduces Hydra-0, a generalist world model conditioned on action flow: image-plane trajectories that enable one model to learn across human hands, handheld grippers, single-arm robots and bimanual systems. Explore the project 📄

ROBOT FOOTBALL WORLD CUP ⚽️ RoboCup 2026, the annual autonomous-robotics world championship, was held in Incheon, South Korea in early July 2026, with ~3,000 participants from 45 countries. Its headline event is the Humanoid League, where teams run their own AI/software on humanoid robots to play fully autonomous soccer, and this year staged the first-ever 11-vs-11 humanoid match. Division winners: - Small -> Invic (Wuhan University, China) on a Booster K1 Air - Middle -> B-Human (Universität Bremen/DFKI, Germany) on a Booster K1 - Large -> Tsinghua Hephaestus (China) on a Booster T1, which retained the top-division world title by beating China Agricultural University's Shanhai 6–2 in the final. 38 of the 59 humanoid-league teams ran Booster Robotics machines, and Booster robots took gold, silver, and most of the podium across all three divisions. The Beijing hardware company became the de-facto standard platform. Germany won the Middle division (B-Human) plus best-software honors, but running on Chinese hardware. B-Human beat fellow German side HTWK Robots 4–0, i.e. Germany vs Germany, both on Booster robots. Beyond selling the robots, Booster has launched Booster Studio (a development environment) and its own 3v3 robot football league. Ecosystem capture in action, with the SDK and their own the competitionl, the same move NVIDIA made with CUDA.