Xiaolong Wang

@xiaolonw

Co-leading robotics @Meta Superintelligence Labs Co-founder of ARI

Menlo Park, CA

xiaolonw.github.io

Joined March 2016

1,543 Following

23.2K Followers

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Xiaolong Wang
@xiaolonw
Aug 20
Muse Spark, now making its way onto robots. Some early results of our work at Meta 👀
Alexandr Wang
@alexandr_wang
Aug 20
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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Xiaolong Wang
@xiaolonw
Oct 1
Looks solid.
Alberto Rodriguez
@_albertorod_
Oct 1
Today we give Atlas dexterous hands, with the strength to take on real work, and the simplicity for mass manufacturing. Like the body, the hands are designed with high fidelity simulation in mind, to enable sim-to-real RL. If you give dexterous hands to a robot, make them do
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Xiaolong Wang
@xiaolonw
Sep 30
If you dont know Yutong, he is the guy always have the rubik's cube in his hands. A lot of work is needed to make this happen in real.
Yutong Liang
@YutongLiang_
Sep 30
Meet FINGR: a dexterous hand that solves a 2×2 Rubik’s Cube with continuous finger tricks. Here’s how we got the fingers to work together. 🧵
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Xiaolong Wang
@xiaolonw
Sep 19
Super exciting!
Shuyan Zhou
@shuyanzh36
Sep 19
some belated life updates: earlier this year, i left academia and joined meta to work on personal superintelligence. these days i’ve mostly been trying to make our models really good at browser use. back in 2020, i wrote in my phd sop that i hoped one day my mom could use a
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Zhiqing Sun
@EdwardSun0909
Sep 19
Computer use has been one of Muse Spark’s core agentic capabilities since MS 1.1. The model is trained end-to-end to automatically decide when to use scripts or when to use clicks to reduce latency Kudos to our amazing CUA team @shuyanzh36 @yashvarpatel @ZiYiDou @TianbaoX
Xiaolong Wang
@xiaolonw
Sep 11
We indeed need large moe.
John Carmack
@ID_AA_Carmack
Sep 10
If the target market is real time robotics, the Jetson Thor system, with 128 GB of memory, but only 273 GB/s of bandwidth, seems over-provisioned with expensive memory. You want the model evaluating at tens of fps, so it can't use more than 10 GB of weights at most. More memory
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