🚨 3 days left 🚨
CppCon 2026 Robotics & AI track CFP closes Sunday, May 17 🤖⏳
Pitch us your best C++-for-robots talk: controls, planning, sim, ML, safety, perf 🔥
Me + @ItMaybeTyler want code on slides 💻
👉 https://t.co/FK9m4rrb9G
Yann LeCun closed $1.03B for AMI Labs on March 10. Three days later, this paper dropped from his NYU collaborators.
15M parameters. Single GPU. A few hours of training.
LeWorldModel is the first JEPA that trains end-to-end from raw pixels. Two loss terms: predict the next embedding, keep the latent space Gaussian. Previous JEPAs needed exponential moving averages or pretrained encoders to avoid representation collapse. LeWM doesn't.
Six hyperparameters down to one.
The numbers are the story. Foundation-model-based world models require hundreds of millions of parameters and serious compute to plan a control task. LeWM plans up to 48x faster while staying competitive on 2D and 3D benchmarks. The whole thing fits on a laptop GPU.
Look at the trajectory. Yann announced his Meta departure in November 2025 after 12 years and called founding FAIR his "proudest non-technical accomplishment." On March 10, 2026, AMI Labs closed the largest seed round in European history at a $3.5B pre-money valuation. Bezos, Nvidia, Samsung, and Toyota all wrote checks.
Three days later: a paper showing that JEPA-from-pixels is no longer fragile and no longer compute-heavy. The engineering scaffolding that made it look like an academic curiosity is gone.
The authors sit at Mila, NYU, Samsung SAIL, and Brown. None at Meta.
Yann's bet was that the path to machine intelligence runs through world models, not language models. He left a public company to build it. Each JEPA paper from his network resets the assumed cost structure for that bet. This one makes world modeling laptop-cheap.
Meta still has the GPUs. The architecture left.
🚨🚨🚨 Alert🚨🚨🚨
Someone purchased the page "plotjuggler DOT com" and is impersonating me.
This is most likely a Malware / Phishing scam. Do not download anything from there.
I am taking urgent actions. Please repost for visibility
Excited to share @tutorintel's Data Factory 1, a 100 robot semi-humanoid research farm and the largest robot data factory in the United States.
Our first embodiment “Cassie” is deployed at industrial scale across the supply chain. We built DF1 to bootstrap fleet-scale learning for our "Sonny" industrial semi-humanoid embodiment, powered by our first end-to-end robot foundation model Ti0.
JEPA are finally easy to train end-to-end without any tricks!
Excited to introduce LeWorldModel: a stable, end-to-end JEPA that learns world models directly from pixels, no heuristics.
15M params, 1 GPU, and full planning <1 second.
📑: https://t.co/cpTzgvbTS0
Meet “Roadrunner,” a new 15kg (33 lb) bipedal wheeled robot prototype featuring multi-modal locomotion: side-by-side and inline wheel modes and and stepping. Its symmetric legs articulate at the knee for obstacle avoidance.
We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.
Back in Nov we developed Recap and trained π*-06 with RL. Now, we developed a fast *online* RL method that improves π-06 with as little as 15 min of robot data for precise tasks, using "RL tokens" exposed by our model that can be fed into a small actor-critic method.
Another great article that summarizes something that I have been thinking a lot:
"Grief and the AI Split".
Many of us feel a sense of grief: skills that took a long time to acquire are now being commoditized by AI.
And we see two clear groups of people (thread) 🧵👇
Unveiling our new startup Advanced Machine Intelligence (AMI Labs).
We just completed our seed round: $1.03B / 890M€, one the largest seeds ever, probably the largest for a European company.
We're hiring!
[the background image is the Veil Nebula - a picture I took from my backyard, most appropriate for an unveiling]
More details here:
https://t.co/eWHyGLXwCA
Advanced Machine Intelligence (AMI) is building a new breed of AI systems that understand the world, have persistent memory, can reason and plan, and are controllable and safe.
We’ve raised a $1.03B (~€890M) round from global investors who believe in our vision of universally intelligent systems centered on world models. This round is co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions, along with other investors and angels across the world.
We are a growing team of researchers and builders, operating in Paris, New York, Montreal and Singapore from day one.
Read more: https://t.co/kyVAL7EoFx
AMI - Real world. Real intelligence.
For a long time, I was skeptical about action-conditioned video prediction for robotics. Many models look impressive, but once you ask them to handle long-horizon manipulation with real physical interaction, things quickly fall apart (e.g., Genie is amazing but mostly focused on navigation).
This project changed my mind.
I'm beyond excited to share Interactive World Simulator, a project we have been working on for the past ~1.5 years 🤖
One of the first world models that produces convincing results for long-horizon robotic manipulation involving complex physical interactions, across a diverse range of objects (rigid objects, deformables, ropes, object piles). It directly unlocks scalable data generation for robotic policy training and policy evaluation.
Try it yourself (no installation needed): https://t.co/u1ZP34yVET
Play directly with the simulator in your browser.
Key Takeaways:
1️⃣ 15 Hz long-horizon action-conditioned video prediction for 10+ minutes on a single RTX 4090 GPU
2️⃣ Visual and dynamic fidelity: people often ask how much sim data equals one real data point. In our experiments, it turns out to be close to one-to-one using the Interactive World Simulator
3️⃣ Stress testing matters: we emphasize interactive stress testing to understand robustness and stability and to build trust in the simulator
4️⃣ The model is trained with only ~6 hours of real-world random interaction data on a single GPU. Imagine what happens if we scale this 1000× or even 1M×
Huge credit to @YXWangBot, who led this effort with countless hours of work on data collection, training recipes, and system design. I'm incredibly proud of the work he did here!
Enjoy the demos and videos. We also fully open-sourced the codebase for anyone interested in applying this to their own tasks.
#Robotics #RobotLearning #WorldModels #EmbodiedAI
We’ve developed a memory system for our models that provides both short-term visual memory and long-term semantic memory.
Our approach allows us to train robots to perform long and complex tasks, like cleaning up a kitchen or preparing a grilled cheese sandwich from scratch 👇