All forms of intelligence co-emerged with a body, except AI We're building a #future where AI evolves as your lifelike digital twin to assist your needs across health, sports, daily life, creativity, & beyond... myolab.ai ➡️ Preview your first #HumanEmbodiedAI

Fifteen years ago, I had the privilege of working with Rosen Diankov at CMU on his PhD thesis. The capstone was IKFast — for a generation of roboticists, the definition of what analytical inverse kinematics could be. Today, I'm excited to release the next chapter: ssik. 1/

Are you still running your robot policies on vision encoders trained purely on static images? Nowadays, the standard practice in robot learning is to plug in powerful vision models like CLIP, SigLIP, or DINOv2. This inherits a quiet, convenient assumption: “Let mainstream computer vision handle perception, and the downstream policy will figure out the dynamics.” But let’s be real for a moment. Is this truly the best we can do? We introduce DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation.⬇️ 🔷 Dynamics upstream: we push motion understanding into perception. 🔷 Tri-modal-dynamics supervision: image transitions × language × 3D flow, fused via simplex-volume alignment (260K trajectories from robot & human video) 🔷 Transfers everywhere: a visual backbone for diverse policies (MLP, Diffusion Policy, VLA) 🔷 22.5% over the strongest baseline (DINOv2, SigLIP) under real-world OOD 🔷 Open-Source & easy to use 🌐 Website: dynaflip-robotics.github.io 📄 Paper: arxiv.org/abs/2605.30350 💻 Code: github.com/JU-SUK/DynaFLIP 🤗 Hugging Face: huggingface.co/jlee-larr/dyn…

We are back again :) After three weeks of quiet building. Introducing Genesis World 1.0, our latest simulation platform, the second release in our full-stack suite. Open-sourced. Robotics is still bottlenecked by the 1× speed of the physical world. Every model, checkpoint, and data recipe eventually needs to be tested on physical hardware, slowly, expensively, and with limited coverage. One hour in reality can become 100 days in simulation. That is how robotics model iteration moves from a wall-clock bottleneck to a compute problem. To make this work, simulation has to be both fast and trustworthy. Over the past year, we rebuilt the entire stack: a GPU-accelerated cross-platform compiler, penetration-free multi-physics contact solvers, unified rigid and deformable physics, and a photo-realistic renderer purpose-built for physical AI applications. We built Nyx, a high-performance path-traced rendering engine for robotics application. Genesis World 1.0 achieves near realtime performance with our latest development for penetration-free IPC solver, supporting various types of deformables beyond rigid bodies. It supports contact-rich, dexterous manipulation simulation across different embodiments: unitree, sharpa, wuji, genesis hand and various types of grippers. Under the hood is Quadrants, our effort in pushing forward cross-platform GPU-accelerated computation. Quadrants started as a fork of Taichi, and we rebuilt most of the critical parts for optimizing simulation workloads, giving 10x faster launch time and up to 4.6x runtime performance compared to the initial Genesis release. Together, they bring us to an unprecedentedly low sim-to-real gap, enabling zero-shot real-to-sim model evaluation and much faster iteration of GENE. All available today. Genesis World 1.0: github.com/Genesis-Embodied-… Quadrants: github.com/Genesis-Embodied-… Nyx: github.com/Genesis-Embodied-…

This is evoBOT, a robot helper developed by Germany’s Fraunhofer Institute for Material Flow and Logistics. It can grasp and carry goods to support cargo workers in transporting packages. evoBOT can also move smoothly across uneven terrain, including bumpy surfaces and sloping ground.

Spent last week benchmarking policy speedup methods. Then we just collected faster data and it beat all baselines... Although obvious, but turns out first step to speed up your policy is … collect faster data.

Introducing HRM-Text. An ultra-lean 1B-parameter reasoning language model designed to deliver strong general performance with a fraction of the data, compute, and infrastructure. Trained on just 40B structured tokens, HRM-Text achieves competitive performance while using ~1/1000 of the training data of comparable models. The kicker? The full model trains in roughly one day on a $1,000 budget. This opens the door to a new generation of AI that is powerful, accessible, and radically easier to adapt. Theories and research concepts once deemed too expensive to test are officially back in the game. Sapient Intelligence invites you to help us shape a new paradigm for general intelligence.

You can’t lift a fridge with just your hands. Your whole body needs to conform to its shape, and bear the load between your arms and torso. Here, @BostonDynamics' Atlas uses proprioception to manage the whole-body interaction and adapt to a shifting 100 lb load. Enabling this type of high performance manipulation is exactly why we walked away from what was arguably the world’s best implementation of MPC for humanoids, and shifted entirely to RL without looking back. This level of whole-body controls is a fundamental building block of physical intelligence and key to the value proposition of humanoids. More technical details in: Blog: bostondynamics.com/blog/trai… Behind the scenes video: youtu.be/xKK5ze3FukQ

Voice‑driven, real‑time arbitrary action generation😁 Using external voice commands, G1 is directly controlled to generate a wide range of actions in real time. This video was recorded in a single take, with on‑site audio recording. Because the actions are autonomously generated by AI in real time, there may be slight latency, and the smoothness of the movements may be somewhat reduced.

Congrats to Aime!! He said his left forearm is basically broken 😂 Final scores: → F.03: 12,732 packages (2.83 seconds/package) → Aime: 12,924 packages (2.79 seconds/package) This is the last time a human will ever win

Mind has raised an additional $400M, bringing our total funding to over $1 billion. We are building dexterous, general-purpose robots and foundation models for industrial deployment, starting with the automotive industry. I’m super excited and grateful to be working on these problems with such a stellar team. We are uniquely positioned to tackle the general-purpose robotics problem given our deep partnership with @Rivian, who is a shareholder and our pilot customer, and who is also providing us with data from production lines to add to our training mixture. It’s still super early days, and we’re hiring across research, software, hardware, and more! (Link below)

Day 2 is Live: Watch humanoid robots Bob, Frank, and Gary running 24/7. This is fully autonomous running Helix-02 x.com/i/broadcasts/1AxRnakog…

Big announcement for Humanoid: we’ve secured a landmark deal with @SchaefflerGroup to deploy thousands of humanoid robots. Humanoid has signed a binding, phased deployment with Schaeffler, a leading Motion Technology Company, to integrate humanoid robots directly into live manufacturing operations. The first systems go live in Germany before the end of 2026. The agreement is structured around a Robot-as-a-Service (RaaS) model. Under this model, Humanoid provides the robotic systems together with the related services required for end-to-end deployment and operation, including connection to the fleet management software, maintenance, 24/7 technical support, updates, and ongoing performance management. In addition to the deployment contract, Humanoid has also signed a 5-year supply agreement with Schaeffler for supply of actuators. More details here: thehumanoid.ai/humanoid-secu…

Hot take: most people who say "humanoid robots are 5 years away" are looking at the wrong bottleneck. It's not hardware. It's not compute. It's generalization! The demo problem: Every impressive robot video you've seen was recorded in a controlled environment. The robot knows the room. It knows the objects. The lighting is set. The task is narrowly defined. That's not a failure, it's just not real-world deployment. The gap between "impressive demo" and "ships to customers" in robotics is called sim-to-real transfer. Training in simulation is cheap and fast. Making that training hold up in the messy physical world is hard. This is the actual frontier right now. What IS real progress (not hype): → Foundation models for action (RT-2, π0, similar) are genuinely changing what's possible → Hardware costs dropping dramatically (motors, sensors, compute) → Data flywheels are starting to spin These three converging = the inflection I'm watching closely. My actual timeline estimate: - 2026–2027: Commercial deployment in controlled factory environments at scale - 2027–2029: Semi-structured environments (warehouses, care facilities) - 2029 : Generalized home/consumer robotics Anyone who's more aggressive than this hasn't spent time on sim-to-real. #ai #robotics #autonomousdriving #humanoidrobot

This is the single best read on World Models and one of the most important reads in AI. $10B has flowed into "world models" in the last 18mos, from Yann LeCun to FeiFei Li. The promise is, like LLMs, world models will provide the data it takes to scale robotics foundation models, and solve robotics. ..but the word has been abused to mean one of many things. This post unpacks: – What 5 traits makes a world model? – How do the different approaches stack up? – What is it used for within and beyond robotics? – Where is the opportunity? – Citations to research, news and blog posts Companies / products in the space include: – BigCo products: Google Genie, Tesla Optimus, Nvidia DreamDojo, DreamZero, Microsoft Muse – Pure world model: AMI Labs, World Labs, Runway, Rhoda, Decart, Spaitial, Odyssey, Embo, Dream Labs, OneWorld – Robot foundation model cos: Skild, Physical Intelligence, Figure, Mind Very likely one of the seminal technologies of the next decade.