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We just open-sourced luwu_mjlab: an RL training environment for Luwu Dynamics quadruped robots. Built on mjlab + MuJoCo Warp, it lets us train locomotion and get-up policies in simulation, then validate them on real robots. GitHub:

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XGO Robot

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We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim Massachusetts Institute of Technology (MIT). We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency! PDF: Video: We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes. This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods. Kudos to Yunlong Song! UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) Massachusetts Institute of Technology (MIT)MechE
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Our CTO boyuan is demoing our new localization algorithm, which surpasses leading industry solutions. Localization enables a robot to determine its position in an environment, which is essential for navigation. While most systems require robots to start from a predefined location, ours doesn’t. It lets robots boot up anywhere and immediately locate themselves, making deployment far more flexible for real world scenarios.

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Humans are cooked. 😳 China just hosted the world's first robot-led gala show, 60 minutes of humanoid robots performing live on stage. Not a tech demo. Not a 30-second clip from a lab. A full entertainment show during the Chinese Spring Festival with 12 performances including dance, magic, comedy, martial arts, and a fashion runway. The robots did backflips. Let me say that again. The robots. Did. Backflips. AGIBOT G2 humanoid robots and D1 quadruped robots performed in perfectly synchronized formations…. rapid turns, group choreography, runway walks.. transitioning seamlessly between segments for a full hour. But here's what actually blew my mind: In one segment, human dancers performed alongside the robots in coordinated routines. Real-time alignment between human movement and robotic motion. You couldn't tell who was leading. In another, quadruped robots dressed as pandas danced with children on stage. And in the corner? A sign-language translation robot was providing barrier-free access for hearing-impaired viewers. This wasn't entertainment. This was a statement. Some context most people are missing: AGIBOT shipped over 5,000 humanoid robots in 2025. They're ranked #1 globally in humanoid robot shipments. The company was founded by a former Huawei "Genius Youth" engineer just 3 years ago. Three. Years. Meanwhile, Boston Dynamics has been at it for 32 years and still doesn't have a consumer product on the market. China isn't just catching up in robotics. They're lapping us while doing backflips. And the craziest part? You can now rent these same robots for events through their platform, starting at 999 yuan. That's about $140. The future isn't coming. It's performing on stage, doing backflips, and it costs less than your Costco run. AGIBOT #AGIBOT NIGHT #RobotsHumanoides #ArtesMarcialesChinas

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Robot simulation is getting a big upgrade! 🔥 Normally, building a simulation environment, a kitchen, warehouse, office takes days or weeks. You have to model the geometry, add textures, set lighting, generate collision meshes… all before a robot can even interact with it. Marble by World Labs removes most of that work. You type a text prompt or give it an image, and it generates a full 3D scene with depth, lighting, and a collider mesh you can export into engines like MuJoCo. That means researchers can quickly create lots of different environments to train robots on, which is exactly what domain randomization needs. If we want robots to learn faster, we need to create worlds faster. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

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Testing image-to-CAD for SR Platform on a new set of GPUs 🔥 We are working with lium.io (SN51) to showcase the efficiency of decentralized compute for SR Platform — from CAD generation to RL training and sim-to-real validation What it means: > Flexible, on-demand rental for us. Pay with crypto. > Diverse GPU options, powered by Openτensor Foundaτion's incentive alignment. > Lower equipment cost → higher margin for the network. Onchain robots, onchain compute.

Strike Robot

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A pint-sized robot named Erbai in China has gone viral after it "kidnapped" dozens of larger robots at a robot showroom in China. People are finding the incident both hilarious and territying. The event, captured on CCTV, shows Erbai -an Al-powered robot - engaging in human-like conversations with other robots at the showroom to convince them to "go home" with it. The footage shows Erbai roaming the hallways at midnight, it then stops before a row of larger robots and asks one, "Are you still working overtime?" "We don't get off work," the larger robot responds. "Are you going home?" Erbai persists. "I don't have a home," the larger robot replies. Erbai then invites the bigger robots to "go home" and, surprisingly, the robots followed its lead, chanting, "Go home, go home!" The company behind Erbai confirmed that the "kidnapping" was real, though pre-planned. They explained it was a test designed to evaluate Erbai's ability to interact with and influence other robots. The team collaborated with an Al firm to see whether Erbai could autonomously "recruit" other robots.

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