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

📢Sonata: Self-Supervised Learning of Reliable Point Representations📢 Meet Sonata, our"3D-DINO" pre-trained with Point Transformer V3, accepted at #CVPR2025! 🌍: xywu.me/sonata 📦: github.com/facebookresear… 🚀: github.com/Pointcept/Poin… 🔹Semantic-aware and spatial

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.

New work from the Robotics team at AI at Meta . 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: locate3d.atmeta.com/demo model & code & dataset: github.com/facebookresear…

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

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

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.

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

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. 🧵

TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: toyotaresearchinstitute.github.io/lbm1/ 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

Viser is such an amazing tool for the community - congrats to Brent Yi and co!

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!

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

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, dex-manipulation.github.io/corl2025/

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.

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.