AI RESEARCH
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
arXiv CS.CV
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ArXi:2511.07820v3 Announce Type: replace-cross Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, target a limited set of behaviors, and are trained on a handful of GPUs. We show that scaling model capacity, data, and compute yields a generalist humanoid controller capable of natural, robust whole-body movements.