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Omar Rayyan
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Omar Rayyan

@omarrayyann
sr @googledeepmind, phd-ing @ucla
orayyan.com
Joined June 2014
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  • Pinned
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    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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    Omar Rayyan
    @omarrayyann
    2h
    Training for the robot olympics with MuJoCo’s new surfacevel for geoms
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    Omar Rayyan
    @omarrayyann
    Jul 13
    I’m at RSS in Sydney 🇦🇺 to present: - MolmoSpaces, Tuesday 6:30pm - Contact Anchored Policies, Wednesday 4:00pm - a (tbr) work on training general visual loco-manipulation policies in sim. If you work on anything from learning with off-domain data to sim-evals, let’s chat!
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    Omar Rayyan
    @omarrayyann
    Jun 9
    Wall-OSS is now the #1 policy on the zero-shot MolmoSpaces evals. A lot of details in their paper, I recommend checking it out.
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    X Square Robot
    @XSquareRobot
    May 28
    We are open-sourcing Wall-OSS-0.5. Pretrain Once, Act Anywhere. Wall-OSS-0.5 is a VLA model for real-world robotic manipulation, exploring whether pretraining alone can produce robot capabilities directly testable on physical hardware before task-specific fine-tuning.
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    Omar Rayyan
    @omarrayyann
    Mar 11
    Check out our MolmoBot release and the open-sourced foundational models trained entirely in simulated MolmoSpaces homes!
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    Ai2
    @allen_ai
    Mar 11
    Today, a step forward in open robotics - our results show that sim-to-real zero shot transfer for manipulation is possible. MolmoBot is our open model suite for robotics, trained entirely in simulation on MolmoSpaces.🧵
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