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Kay - Liyiming Ke
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@xkelym

Kay - Liyiming Ke

@xkelym
Robotics Research @physical_int | EECS Rising Star; Prev CS PhD @UW built my 🥢 robot; intern at MetaAI, Microsoft Research, Google Search
San Francisco, CA
kayke.xyz
Joined February 2013
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  • Pinned
    @xkelym
    Kay - Liyiming Ke
    @xkelym
    Mar 20
    Faster, stronger, better 🦾
    @physical_int
    Physical Intelligence
    @physical_int
    Mar 19
    We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.
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  • @xkelym
    Kay - Liyiming Ke
    @xkelym
    Jul 31
    Knowing our sim is in good hands, I can sleep a little more peacefully now 😌
    @Stone_Tao
    Stone Tao
    @Stone_Tao
    Jul 31
    Just over a month later, I have now joined @physical_int full time! Wrote a bit about why I'm excited to research simulation at Pi stoneztao.substack.com/joining-physic…
  • @xkelym
    Kay - Liyiming Ke
    @xkelym
    May 6
    Coolio! Love the piano demo.
    @gs_ai_
    Genesis AI
    @gs_ai_
    May 6
    We are back. After one year of quiet building. Introducing GENE-26.5, our first robotic brain that takes a major step toward human-level capability. For years, robotics has struggled to learn from the world’s largest and valuable data source: Humans. Solving it means
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  • @xkelym
    Kay - Liyiming Ke
    @xkelym
    Nov 19, 2025
    Super cute, congrats on the release!
    @sundayrobotics
    Sunday
    @sundayrobotics
    Nov 19, 2025
    After 18 months in stealth, dozens of prototypes, millions of real-home demonstrations, and one final all-nighter, we’re thrilled for you to say hello to Memo
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  • @xkelym
    Kay - Liyiming Ke
    @xkelym
    Nov 18, 2025
    Need to make RL great again 😉
    @physical_int
    Physical Intelligence
    @physical_int
    Nov 18, 2025
    Our model can now learn from its own experience with RL! Our new π*0.6 model can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes. More in the thread below.
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