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Sergey Levine
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Sergey Levine

@svlevine
Associate Professor at UC Berkeley Co-founder, Physical Intelligence
Berkeley, CA
rail.eecs.berkeley.edu
Joined April 2018
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  • user avatar
    Sergey Levine
    @svlevine
    Aug 26
    Seohong wrote a mystery novel. You won't believe whodunit
    user avatar
    Seohong Park
    @seohong_park
    Aug 24
    Behavioral cloning mystery seohong.me/blog/behaviora… I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!
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  • user avatar
    Sergey Levine
    @svlevine
    Aug 26
    Thanks Ryan for coming by! This was a fun chat.
    user avatar
    Ryan Peterman
    @ryanlpeterman
    Aug 24
    Sergey Levine (@svlevine) is one of the world's top robotics researchers and co-founder of Physical Intelligence. We talked about where humanoid robotics is today, thoughts on the Chinese robotics ecosystem, and his predictions for future timelines. In this episode: •
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  • user avatar
    Sergey Levine
    @svlevine
    Aug 15
    Latest Deep RL class lectures are now online! youtube.com/playlist?list=… Thanks to @seohong_park, we now have CS185/285 for spring 2026 available to everyone to watch. Course website here: rail.eecs.berkeley.edu/deeprlcourse/ Apologies for a few recording glitches (it's not a perfect system).
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    youtube.com
    CS 185/285: Deep Reinforcement Learning (Spring 2026)
    Recordings of CS185/285: Deep Reinforcement UC Berkeley Spring 2026 See course website: https://rail.eecs.berkeley.edu/deeprlcourse/
  • user avatar
    Sergey Levine
    @svlevine
    Aug 14
    Chelsea doing another rock star presentation. Robots can indeed fold laundry and make espresso!
    user avatar
    Y Combinator
    @ycombinator
    Aug 12
    Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, @physical_int cofounder @chelseabfinn explains
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  • user avatar
    Sergey Levine
    @svlevine
    Aug 7
    Action chunking is a mysteriously effective method. Modern large-scale imitation learning basically doesn't work without it. But why does it actually help? In our new paper, we try to break down the reasons. As the saying goes, what happened next might surprise you...
    user avatar
    Andrew Wagenmaker
    @ajwagenmaker
    Aug 7
    Action chunking is a critical component in virtually all modern approaches to imitation learning for robotics. But why is it so critical, and do we really need action chunking? Check out our latest work to find out! (1/n) action-chunking.github.io
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