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Omar Rayyan

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@omarrayyann

orayyan.com Joined June 2014
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Omar Rayyan Verified account @omarrayyann
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 for vision-based loco-manipulation. More below🧵
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Omar Rayyan Verified account @omarrayyann
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 Verified account @omarrayyann
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 Verified account @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.

Key technical highlights:

• Gradient-bridged co-training
• Vision-Aligned RVQ Action Tokenizer
• Action-Space Supervision
• DMuon distributed optimizer

In zero-shot real-robot evaluation, the pretrained checkpoint achieved task-progress scores above 80 on multiple tasks, including Block Sorting, Fruit Sorting, Ring Stacking, and Rope Tightening.

Paper, code, blog, and uncut videos: x2robot.com/oss#resources
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