I'm a featured interview in our latest behind-the-scenes release! We break down the ML and perception that drives the whole-body manipulation behaviors from last year. It starts with a neat demo of Atlas's range-of-motion and our vision foundation models.

Same for ego data, UMI data, etc. An open secret is “we used ego4d” actually means filtering out the 1% of videos that are vaguely useful for learning. Occlusions, suboptimality, sensor noise, and so many pitfalls! Modeling + collect co-design is only gold standard currently.

I'm super excited to announce mjlab today! mjlab = Isaac Lab's APIs + best-in-class MuJoCo physics + massively parallel GPU acceleration Built directly on MuJoCo Warp with the abstractions you love.

I'll be giving a talk @CoRL2025 about developing large behavior models on Atlas. Come by the dexterous manipulation workshop at 11:30 to see the talk,

Can we scale up mobile manipulation with egocentric human data? Meet EMMA: Egocentric Mobile MAnipulation EMMA learns from human mobile manipulation + static robot data — no mobile teleop needed! EMMA generalizes to new scenes and scales strongly with added human data. 1/9

Lucas and co. wrote a great blogpost on the careful science and engineering behind language-conditioned policies for whole-body manipulation! There's a lot more work on the horizon; our team is hiring researchers to scale egocentric human data and VLMs for robotics. Reach out!

I had a great time presenting at the workshop - Check out my talk and the other amazing panelists here! https://t.co/6qlZMv0fwP

Viser is such an amazing tool for the community - congrats to @brenthyi and co!

TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: https://t.co/n0qmDRivRH One of our main goals for this paper was to put out a very careful and thorough study on the topic to help people understand the state of the

Current robot policies often face a tradeoff: they're either precise (but brittle) or generalizable (but imprecise). We present ViTaL, a framework that lets robots generalize precise, contact-rich manipulation skills across unseen environments with millimeter-level precision. 🧵

I'll be speaking at the RSS Dexterous Manipulation Workshop tomorrow, discussing our recent work with Atlas!

Today we're excited to share a glimpse of what we're building at Generalist. As a first step towards our mission of making general-purpose robots a reality, we're pushing the frontiers of what end-to-end AI models can achieve in the real world. Here's a preview of our early

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

Our team also put together a great blogpost: https://t.co/MaxojplNQ1. We talk about supervised learning for 2D keypoints, foundation models for object masks, render-and-compare for object pose, and SuperTracker—our real-time smoother that combines vision, kinematics, and force.

Check out the awesome follow-up to sparsh by @akashshrm02 and others - self-supervised learning for a variety of downstream tasks with tactile skins!

Honored to be a part of the RSS Pioneers cohort this year - look forward to catching up with folks in Los Angeles! 🤖

New work from the Robotics team at @AIatMeta . Want to be able to tell your robot bring you the keys from the table in the living room? Try out Locate 3D! interactive demo: https://t.co/aS9WPPmhcF model & code & dataset: https://t.co/oMWc32VrH9

So excited for this!!! The key technical breakthrough here is that we can control joints and fingertips of the robot **without joint encoders**. Learning from self-supervised data collection is all you need for training the humanoid hand control you see below.

📢Sonata: Self-Supervised Learning of Reliable Point Representations📢 Meet Sonata, our"3D-DINO" pre-trained with Point Transformer V3, accepted at #CVPR2025! 🌍: https://t.co/x8k3v1kBw9 📦: https://t.co/dmcEAOafkE 🚀: https://t.co/VWTFyIj7De 🔹Semantic-aware and spatial

Atlas is demonstrating reinforcement learning policies developed using a motion capture suit. This demonstration was developed in partnership with Boston Dynamics and @rai_inst.