In collaboration with @boosterobotics, XMAQUINA is giving a Booster K1 humanoid robot to the community. Stake $DEUS, contribute to the DAO, and earn entries into the draw. Meet the Booster K1 ↓

NEW UNITREE COMPETITOR JUST DROPPED! Booster Robotics @boosterobotics is a Beijing humanoid-robot startup founded in June 2023 by CEO Cheng Hao, with a core team from Tsinghua's Robot Control Lab and its Hephaestus RoboCup soccer team. It sells small, walk-and-run-optimized bipedal humanoids as a research, education and competition platform rather than for industrial labor. Its product line consists of: - the K1 (about 95 cm, 19.5 kg, 22 DoF, NVIDIA Jetson Orin NX, ROS2 and Isaac Sim support), starts at $4,999. - The flagship T1 (about 118 cm, 30 kg, 23 DoF, Jetson AGX Orin, up to 18 km/h), runs roughly $22,000 to $34,000. - The Professional T2 (July 2026), that uses NVIDIA's Thor compute. It ships a developer IDE, Booster Studio, and its robots won both humanoid leagues at RoboCup 2025. Booster Robotics targets research, education and competition, has shipped more than 700 units to over 200 customers (more than half internationally), and reached positive monthly operating cash flow in December 2025, while the marquee humanoid companies (Figure, Tesla Optimus, even Unitree pre-IPO) burn cash chasing a factory-labor future. Booster already monetizes the developer ecosystem (cheap robots plus the Booster Studio IDE plus ROS2 and Isaac Sim) instead of promising labor replacement later. -> It is the anti-Figure play. Both 2025 RoboCup humanoid league champions ran on Booster hardware! Tsinghua's Hephaestus won AdultSize on the T1 and Germany's Boosted HTWK won KidSize on the K1 Winning robot soccer is an unfakeable test of dynamic bipedal control (unstructured, adversarial, contact-rich, you cannot cherry-pick a match), and sweeping both size classes, including a German team choosing a Chinese robot, means the serious humanoid-research community standardizes on Booster. Pricing is not random. The K1 at $4,999 puts a walking humanoid in a classroom and undercuts even Unitree's G1 (about $16,000); the T1 at roughly $22,000 to $34,000 is the RoboCup-grade research machine; the T2 (July 2026, NVIDIA Thor, a claimed ~2,070 TFLOPS) is the pro edition. Sub-$5,000 for a real biped is a bet on the next generation: whoever learns robotics on your platform builds for it. Dexterity is deliberately not their game. The T1 is optimized for walking and running (18 km/h, omnidirectional) and won a soccer league, and Booster describes its IP as a motion-algorithm platform that decouples control across configurations. The compute is NVIDIA (Jetson Orin, Thor), the stack is ROS2 and Isaac Sim, and Booster Studio (June 2026) adds agent development, "vibe coding," simulation and one-click deployment to the physical robot. More than half of units ship internationally, so this is China exporting a developer platform, not just hardware, and riding the global open-robotics toolchain rather than a closed domestic one, the inverse of the usual worry about vertically closed Chinese stacks. Backers include the Beijing Robotics Industry Development Investment Fund, the Beijing Artificial Intelligence Industry Investment Fund and Shenzhen Capital Group, that is, municipal state money seeding a humanoid-developer champion.

Robotics Roundtable Founders and manufacturers discussing where humanoid robotics is heading, what bottlenecks remain, and how embodied AI reaches mass adoption. Co-hosted with @RachelOnchain from @Onchainmedia 🗓️ May 19 | 16:00 UTC 🎙️ @boosterobotics • @hyperspawn • @milesdeutscher

NEW DATA ACQUISITION: Axis enters the chat! Axis Robotics is a San Francisco startup founded in 2025. They build a data engine that generates robot-manipulation training data at scale through a crowd of contributors rather than its own robot fleet. It has four parts: - a task-generation engine that randomizes diverse atomic tasks - a browser-based simulation-teleoperation interface where anyone remotely drives a simulated arm to produce motion trajectories - a mobile app for zero-hardware egocentric real-world capture (i.e. filming a task with a phone) - and a processing pipeline that cleans trajectories, applies domain randomization and adds dense language annotation, with human-gated DAgger intervention loops. It packages the output as customized "Task Packages" sold to robot hardware makers, physical-AI model companies and industrial-automation firms (named partners include Booster Robotics, Manycore Tech, Feagine Robotics, Dexmal, Lotus Car, Geely Auto and SomaStacks). I find it interesting that two of its four components need no robot at all: a browser interface where anyone drives a simulated arm to produce trajectories, and a phone app for egocentric real-world capture with no rig. You do not need an ALOHA setup or a fleet, only a browser and a phone, which is what lets it claim a six-figure contributor base.

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.

From Digital Twins to Data Engine: Cutting the Real-Data Burden with Sim-Powered Robot Learning Teaching a robot a new task could take hundreds of teleoperated demonstrations. For foundation models, adapting to an entirely new robot can cost orders of magnitude more — dedicated hardware, trained operators, months of engineering. On @boosterobotics' dual-arm robot, we studied this at two levels: ✱ Specialist: Can task-aligned simulation mixed with a small set of real demonstrations reduce the real-data burden? ✅ Yes. With only 10 real demos, the policy made no contact at all in physical rollouts (0/20). Adding 50 simulated trajectories brought contact to 17/20. ✱ Foundation: Can data accumulated across tasks build a reusable starting point (a Booster-specific model prior)? ✅ Yes. After full-parameter continued pretraining, a model adapted with just 30 demonstrations per task beat the original given twice as many: 14/16 vs 10/16 in simulated evaluation. Before any task-specific adaptation, in zero-shot simulation, it was already roughly 3× closer to the target (17.27 cm → 5.78 cm). This work runs on Axis Suite, our Physical AI solution across different robot embodiments. Distributed contributors generate task-aligned sim data on Axis Hub at scale, reducing real-data needs for specialist adaptation while powering cross-embodiment generalist training. Read the full blog:

We drove our robots from San Francisco to New York City. Along the way we presented at Automate in Chicago, the biggest robotics convention in North America, took a ferry so our robot could see the Statue of Liberty, and wrapped up the trip with NYC’s first robot pop-up shop in SoHo. Thousands of people visited. Most had never met a robot before, and now they have. Thanks to @KraneShares and our partners: @nvidia, @Visa, @AGIBOT_US, @UnitreeRobotics, @boosterobotics, @LimX_Dynamics, @GalaxeaDynamics, @intbotai, and @ARCL_Katena.