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Mahi Shafiullah 🏠🤖
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Mahi Shafiullah 🏠🤖

@notmahi
Trying to understand the emergence of generally intelligent robotic behavior at @berkeley_ai. Previously @CILVRatNYU @MIT & @Apple AI/ML fellow.
New York, NY
mahis.life
Joined October 2010
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  • Pinned
    user avatar
    Mahi Shafiullah 🏠🤖
    @notmahi
    Jun 18
    Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and
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    Mahi Shafiullah 🏠🤖
    @notmahi
    4h
    It’s all about the (high quality, but maybe not high quantity) data!
    user avatar
    Lerrel Pinto
    @LerrelPinto
    5h
    Turns out that doing In-context learning for robots is not that hard...
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    Mahi Shafiullah 🏠🤖
    @notmahi
    Aug 21
    Learning mobile manipulation is hard because collecting data for mobile manipulation is hard. But seems like @omarrayyann has found a promising way to crack this problem!
    user avatar
    Omar Rayyan
    @omarrayyann
    Aug 21
    Meet FetchMan: a vision-based humanoid policy trained entirely in simulation that transfers zero-shot to diverse real-world scenes and objects. Simulation has produced impressive locomotion policies that transfer to the real world. We wanted to see how far the same recipe goes
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    Mahi Shafiullah 🏠🤖
    @notmahi
    Jul 15
    Presenting the poster at #RSS2026 in 25 minutes, please stop by!
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    00:26
    user avatar
    Mahi Shafiullah 🏠🤖
    @notmahi
    Feb 10
    Best ideas are often the simplest in hindsight. Meet Contact-Anchored Policies (CAP)🧢: by conditioning policies on physical contact (vs language) we achieve env & embodiment generalization with super low resources. This policy ⬇️ learned to pick from scratch w/ 16 hrs of data 🧵
  • user avatar
    Mahi Shafiullah 🏠🤖
    @notmahi
    Jul 14
    We will be in the #RSS2026 poster session at 6:30 PM – stop by with all of your questions!
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    00:18
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    Omar Rayyan
    @omarrayyann
    Feb 11
    MolmoSpaces provides singular scale and diversity. We built a benchmark that puts that scale to use. MolmoSpaces-Bench evaluates zero-shot policies across thousands of environments previously unseen to them under systematic variation, providing insights that go beyond a success
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