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@georgiagkioxari @notmahi we released a dataset previously that is within-domain to our benchmark (huggingface.co/collections/al…). but others got such results with Astra in real-world evals so I wouldn't attribute the performance to this dataset existing
@SOTA_kke they can also just add them to the pool of rl envs they use for post-training. maybe they already do that
@NicholasEPfaff @RussTedrake @phillip_isola Congrats!
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
I think distilling the model’s spatial knowledge into a raw action space policy would be an interesting direction. its good vision understanding makes this verifiable enough to happen in an automated closed-loop way
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/leaderboard) because of the API cost :)
GPT-Astra outperformed all open-source VLA/WAM baselines on a subset of our zero-shot MolmoSpaces-v1 benchmark! traces: orayyan.com/ms-results
Are you learning the noise of size action_dim*chunk_size? If so, 40 episodes might be too little for that. For real-world experiments, they learn a noise of size action_dim and tile it across the chunk dimension. Should be more sample efficient (assuming you have no bugs) given the simple pick task you have.
Training for the robot olympics with MuJoCo’s new surfacevel for geoms
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
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
@JulienBlanchon yes will be uploading it to hf
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🧵
@JulienBlanchon Thanks! code will be out soon. but yes, per iteration I sample 64 tasks and roll each 8 times from the same starting state. noise is injected at a single random flow step and logp is taken there. and thanks for the suggestion, I will be trying it out.
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/Fe…
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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