SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Summary
arXiv:2511.07820v4 Announce Type: replace Abstract: Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (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.
Why It Matters
This Robotics & Manufacturing development accelerates factory automation, industrial AI and precision manufacturing across the region. For Asia, it is a signal worth tracking: it shapes who supplies, who scales, and who sets the standard over the next five years.
Key Facts
- SectorRobotics & Manufacturing
- Market—
- ImpactMedium (58/100)
- SignalResearch