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The future of robotics is in your hands. Literally. Excited to announce our new paper,✨R+X✨. A person records everyday activities while wearing a camera.A robot passively learns those skills. No labels, no training. Here's how. 👇

🎉 Excited to share Clarity, an open-source website template I designed to better present and visualise AI research! If you appreciate the minimalist design and want to use it in your own projects, please check it out here: shikun.io/projects/clari…

Excited to introduce Diffusion Augmented Agents (DAAGs)✨. We give an agent control of a diffusion model, so it can create its own *synthetic experience*.🪄 The result is a lifelong agent that can learn new reward detectors and policies, much more efficiently. Here's how. 👇

✨New #IROS2024 paper: “Adapting Skills to Novel Grasps: A Self-Supervised Approach” We leverage self-supervised data to adapt skill trajectories to novel object grasp poses. No need for depth, no prior knowledge, no CAD models, no calibration - just RGB images. Here's how👇

Introducing MarDini 🍸 -- our latest exploration in video diffusion models from AI at Meta! MarDini brings an asymmetric design that breaks down video modelling into two sub-tasks: 1. A masked, auto-regressive, heavy-weight planning model focusing on long-range temporal

Very nice paper. Always exciting to see this kind of "spatial creativity" in agents!

Open-source AI enabled home robot with voice chat. Tell it what to do and it will try to clean up your home for you. This is locally running Qwen2.5 on my desktop. No need to send information from your home to the cloud.

Learning manipulation policies instantly after the demonstrations can unlock so many possibilities… 🤖 Instant Policy does just that! It efficiently tackles In-Context Imitation Learning and relies on cheap procedurally generated data! robot-learning.uk/instant-policy 1/

The graphics community has such beautiful simulation demos. Are these running full physics like in robotics sims, or are there some physics shortcuts which still give realistic renders?

Introducing MASt3R-SLAM, the first real-time monocular dense SLAM with MASt3R as a foundation. Easy to use like DUSt3R/MASt3R, from an uncalibrated RGB video it recovers accurate, globally consistent poses & a dense map. With Eric Dexheimer*, Andrew Davison (*Equal Contribution)

Imagine a robot that learns all sorts of tasks simply by watching you. This future is closer than ever: R+X was accepted at ICRA 2025. From an hour long POV video we can perform in-context imitation learning, from human to robot. No training or robot data needed.

Instant Policy got accepted as an oral at ICLR! 🎉 Exciting times for In-Context Imitation Learning and cheap procedurally generated data! 🦾

Excited to be interning at the Robotics and AI Institute (formerly Boston Dynamics AI Institute). Hoping to learn lots and work on interesting robotics research. If you're in the Cambridge/Boston area and want to meet up, let me know!

10 years ago: #CVPR2025 (Boston) Best Paper Prize CVF archive: openaccess.thecvf.com/content_cvpr_2…

Thrilled to share our work Sakana AI on The AI CUDA Engineer! 👷‍♂️ A framework that autonomously designs & optimizes CUDA kernels. Like OMNI-EPIC, we maintain an innovation archive of diverse solutions to get stepping stones that can be reused for future optimizations!

MASt3R-SLAM code release! github.com/rmurai0610/MAS… Try it out on videos or with a live camera Work with Eric Dexheimer*, Andrew Davison (*Equal Contribution)

1/ Real2Sim and Sim2Real are well-known paradigms in robotics🤖. Is there another paradigm where sim and the real world are more tightly interlinked? We introduce Real-IS-Sim: a framework featuring an always-in-the-loop, correctable simulator for behavior cloning policies.

We are extending the paper submission deadline to August 15, 2025 23:59 AOE. Submit your latest works on generalizable robot priors to the #CoRL2025 #GenPriors workshop! Webpage: corl25-genpriors.github.io

Exciting update on Newton, a new robotics sim from Nvidia/DeepMind/Disney: would recommend following this closely!

I'm very excited to finally announce one of the most ambitious projects we've worked on — which makes the front cover of Science Robotics today: ☀️ Learning a Thousand Tasks in a Day ⭐️ Everyday tasks — like those below — can now be learned from a single demonstration each...