phd student @GoogleDeepMind @UCLA
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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 retweeted
Are we experiencing a GPT moment in robotics? Our MolmoSpaces benchmark seems to suggest so. Check out @omarrayyann's thread to see the details 👇
GPT-Astra outperformed all open-source VLA/WAM baselines on a subset of our zero-shot MolmoSpaces-v1 benchmark! traces: orayyan.com/ms-results
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GPT-Astra outperformed all open-source VLA/WAM baselines on a subset of our zero-shot MolmoSpaces-v1 benchmark! traces: orayyan.com/ms-results
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It used fewer reasoning tokens than other VLM APIs (though the counts might not be directly comparable). I couldn’t run the full benchmark for these API models to include them on our leaderboard (molmospaces.allen.ai/leaderb…) because of the API cost :)
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Omar Rayyan retweeted
A solid step toward humanoid loco-manip, super cool!
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 retweeted
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!
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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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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We follow this sim-to-real recipe to train two policies: a single-object Fetch policy that works across scenes, and an object-conditioned policy that takes the target object's name as an instruction. Both are two-system architectures with the decoupled SONIC controller acting as the low-level controller.
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Check out our website for more videos and interactive simulation episodes: orayyan.com/fetchman.
This project was done with advice from @YuchenCui1 and great collaborators @max_argus, Zhi Li, Chang Yu, Yuxin @chenfanfujiang.
Paper: arxiv.org/abs/2608.17027
Code (soon): github.com/omarrayyann/Fetch…
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Omar Rayyan retweeted
We will be in the #RSS2026 poster session at 6:30 PM – stop by with all of your questions!
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 rate %
More Below:
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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 retweeted
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 dexterous robot data. Some fun insights below: 🧵
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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.
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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Omar Rayyan retweeted
Robotics is still data starved. Collecting high-quality robot demonstrations remains brutally slow and expensive.
Introducing COBALT: A cloud-native teleoperation platform designed for large-scale robot learning.
We are democratizing data collection by leveraging the hardware everyone already owns: the smartphone
All you need is to download an app (today)!
Read on for more!
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