RynnVLA-Latent pretrains a 4B robot policy on 3D latent actions
A 4B policy pretrained on 745.52M descriptors from 30.1K hours of video posts strong LIBERO, VLABench, and RoboTwin scores.
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A 4B policy pretrained on 745.52M descriptors from 30.1K hours of video posts strong LIBERO, VLABench, and RoboTwin scores.
On DROID, end-effector ADE falls 19.5% and LPIPS 11.1% versus DreamDojo, with zero-shot human-to-robot transfer shown qualitatively.
WING transfers interaction knowledge from egocentric human video to robot policies via spectral latent guidance.
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