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Roei Herzig
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Roei Herzig
@roeiherzig
Postdoc @berkeley_ai. Researcher @IBMResearch. PhD @TelAvivUni. Working on Multimodal Robot Foundation Models and Structured Physical AI.
Berkeley, CA
roeiherz.github.io
Joined March 2017
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  • Pinned
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    Roei Herzig
    @roeiherzig
    Jun 18
    If you want a vision encoder for dexterous manipulation, what should be the most important part to model? ๐Ÿค” Current standard models like CLIP, SigLIP, and DINOv2 have an incredible grasp of semantics and spatial details. But they lack the action-centric structure needed for
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    Roei Herzig
    @roeiherzig
    Jun 6
    Article cover image
    Article
    CAIP: Contrastive Action-Image Pre-training for Visuomotor
    This is a (short!) story about how we developed CAIP, an action-centric encoder for dexterous manipulation. If you would like to have a vision encoder for dexterous manipulation (or robotics), what...
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    Roei Herzig
    @roeiherzig
    12h
    How could there be a consensus if nobody knows what their model even *is*? ๐Ÿ˜‚
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    Varun Nair
    @_varunnair
    Aug 6
    Is there consensus that @GeneralistAI the best public physical AI model right now? Who else is close? @physical_int
  • user avatar
    Roei Herzig
    @roeiherzig
    Aug 6
    Another Sunday in Mountain View, yet another tweet that was completely written by Claude, that may or may not have ever happened in real life.
    user avatar
    Ravid Shwartz Ziv
    @ziv_ravid
    Aug 5
    A sunny day in Mountain View. Like every other day. Jeff, Sanjay, Oriol and Quoc walk into a VC office on Sand Hill. The VC is on his second oat latte and has fourteen minutes blocked for this. Jeff: So. We decided to build our own company. VC: Okay, great, love that. Who's
  • user avatar
    Roei Herzig
    @roeiherzig
    Aug 4
    ื•ื•ืื™, ื’ืžื™ื ื™ ื›ื–ื” ืžื•ื“ืœ ืคื—, ืื‘ืœ ืกื•ืœ 5.6 ืžื“ื”ื™ื. ืœื’ืžืจื™ ืฉื™ื—ืงื• ืื•ืชื” oai.
    user avatar
    Boaz
    @boazbe
    Aug 3
    ืžืฉืชืžืฉ ืขื›ืฉื™ื• ื‘Claude Code ืœืคืจื•ื™ืงื˜ ืœื ืงืฉื•ืจ ืœืขื‘ื•ื“ื” ื•ื”ื•ืœื™ ืคืืง ื›ืžื” ื”ืžื•ืฆืจ Overhyped ื•ืœื ื‘ืืžืช frontier. ื ืจืื” ืœื™ ืฉื›ืœ ื”ื”ืชืœื”ื‘ื•ืช ื–ื” ื›ื™ ื›ื•ืœื ืžืฉืชืžืฉื™ื ื‘Fable ืขืœ ื”ืžืงืกื™ืžื•ื Thinking ื•ืขื•ื‘ื“ื™ื ืขืœ ืžืขืจื›ืช CRUD ื’ื ืจื™ืช, ื›ื™ Opus ืขื High ืœื ืžื“ืœื‘ืจ ื“ื‘ืจื™ื ืจืฆื™ื ื™ื™ื, ืื™ื˜ื™ ื‘ื˜ื™ืจื•ืฃ, ื•ื™ื•ืฆืจ slop ื’ืจื•ืข ืฉื—ืฉื‘ืชื™ ืฉื›ื‘ืจ ืขื‘ืจ
  • user avatar
    Roei Herzig
    @roeiherzig
    Aug 3
    It all comes down to how you write the paper. If the paper positioned itself as 'while previous work shows X, we show how scaling can be achieved in diffusion', then sure. But if your main claim is 'We discovered a third pre-training axis beyond parameters and data:
    user avatar
    Aadim Nepal
    @NepalAadim
    Aug 3
    I have read all discussion points and I must say Alexi is 100% correct here. To people who haven't read this work properly: stop all this non sense theatrics. Take a time to read this work properly, and only then you will appreciate it. Alexi is the first person to show this

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