Researcher @Meta Superintelligence Labs Prev: Undergrad @CILVRatNYU @nyuniversity #Robots #ComputerVision #AI

Menlo Park, CA
Joined July 2021
Billy Yan retweeted
Open-weight. 🇺🇸 Made in the US 🇺🇸. Runs on Mac Studio. Muse Spark Open is ready for y'all to play with!
1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲
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Billy Yan retweeted
Full episode dropping soon! Geeking out with @bhawna_paliwal_ @HarithejaE @willjhliang @notmahi on Do as I Do: Dexterous Manipulation Data from Everyday Human Videos do-as-i-do.com/ Co-hosted by @chris_j_paxton @DJiafei
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Billy Yan retweeted
🎉 Excited to share HuRo, accepted to CoRL 2026! Can robotized human videos provide scalable robot learning supervision? HuRo turns human videos into robot aligned observations and actions. In VLA pretraining, OOD completion rises from 34.9% to 72.2%. 🧵 More in replies.
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Great ego data at scale requires hand tracking to succeed in difficult in-the-wild scenarios, not just in carefully collected settings. Here are some examples of our hand tracking in the tricky long-tail:
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Sharing new work on understanding articulated object manipulation from monocular human videos! We explore how far SOTA vision models combined with optimization can take us for articulated object reconstruction and dexterous re-targeting track-articulate-act.github.…
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RT @AdemiAdeniji: This has been a long time coming. Announcing GSI and our first release: the first full-stack product for state-of-the-ar…
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RT @vincentjliu: Today, we're announcing GSI and launching the foundational layer for the most scalable source of real-world robotics data:…
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Grounded API is live. - SOTA on hand-tracking benchmarks (< 1 cm) - SOTA on SLAM benchmarks - In-the-wild ego data -> enriched data in minutes - Integration with @huggingface @LeRobotHF & @rerundotio - Built for @BitRobotNetwork RoboCap suite Technical report & more↓
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Billy Yan retweeted
I’m sensing deep despair in academics over the past week. Astra, Fable, Muse are zero-shotting benchmarks in robotics & world models. Uneasy pill to swallow, but this is what step jumps in progress looks like.
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Billy Yan retweeted
HUG was rejected from CoRL 2026, but people are using it around the world! ↓ on CLOiD robot in Seoul @LGE_korea and Seattle @meta_aria github.com/KevinyWu/hug
Introducing Human Universal Grasping (HUG): dexterous grasping learned entirely from human hands, with zero robot data. 🌐 Website: grasping.io 📄 Paper: arxiv.org/abs/2606.17054 💻 Code: github.com/KevinyWu/hug
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Billy Yan retweeted
Omg, GPT-3
Turns out that doing In-context learning for robots is not that hard...
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I think this is a pretty foundational capability for the next wave of robotics (hence writing about it a bunch) but what separates it from, say, GPT is that a lot of people are going to be able to do it soon
Turns out that doing In-context learning for robots is not that hard...
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It’s all about the (high quality, but maybe not high quantity) data!
Turns out that doing In-context learning for robots is not that hard...
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Billy Yan retweeted
Turns out that doing In-context learning for robots is not that hard...
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Billy Yan retweeted
Muse Spark 1.2 supports a broad range of multimodal tasks, from turning visuals into working code to translating perception into physical action. It also brings robust audio-visual understanding to enable video-heavy workflows common in real-world enterprise use. Today, we’re sharing new evals and demos that illustrate the breadth of the model’s visual understanding and reasoning capabilities. Let’s start with a demo that shows how Muse Spark parses multimodal observations and calls tools to guide a robot to navigate in an unstructured environment to find a rubber duck. 🧵👇
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Billy Yan retweeted
Strong multimodal models = strong robot models. Check out some early glimpses of Meta's Muse Spark 1.2 on robots here!
1/ muse spark 1.2 is a very strong multimodal model—it can do visual coding, robotics planning, and audio-visual understanding that all come together through agentic tools.
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Billy Yan retweeted
Can robots reproduce contact force—not just human motion? Introducing ReForce: force-aware retargeting that turns dexterous human motion + force into robot actions with live tactile feedback. 🌐 website: wuyuhang-eai.github.io/refor… | 📄 paper: wuyuhang-eai.github.io/refor…
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Billy Yan retweeted
🤖 A robot that transforms between humanoid and dexterous hand? Introducing Handroid, a reconfigurable robot with a shared 27-DoF body 🖐️→🚶→🖐️: • The same joints, different roles • Expanded robot task space • Shared sensing and control interfaces handroid.org
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Billy Yan retweeted
We arrived to the Meta offices at midnight, rebuilt YOR and demoed HUG the next day. Had a lot of fun! 🦾😁
We flew our robot to Seattle for the @meta_aria Summit to prove HUG really works. Thanks to everyone who brought objects! Ft @BillyYYan building YOR; PC @irmakkguzey @mangahomanga 🌐 HUG: grasping.io 💻 Code: github.com/KevinyWu/hug 🤖 Robot: yourownrobot.ai/
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Billy Yan retweeted
We flew our robot to Seattle for the @meta_aria Summit to prove HUG really works. Thanks to everyone who brought objects! Ft @BillyYYan building YOR; PC @irmakkguzey @mangahomanga 🌐 HUG: grasping.io 💻 Code: github.com/KevinyWu/hug 🤖 Robot: yourownrobot.ai/
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