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We position motion tracking as a scalable task for humanoid control, leveraging dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (1.2 to 42 million parameters), dataset volume (more than 100 million frames from 700 hours of motion capture), and compute (21,000 GPU hours). Beyond demonstrating the benefits of scale, we further show downstream utility through a real-time kinematic planner that bridges motion tracking to tasks such as navigation, enabling natural and interactive control, as well as a unified token space that supports virtual reality (VR) teleoperation and vision-language-action (VLA) models with a single policy. Through this interface, we demonstrate autonomous VLA-driven whole-body locomanipulation requiring coordinated hand and foot placement. Scaling motion tracking exhibits favorable properties: Performance improves steadily with compute and data diversity, and learned policies generalize to unseen motions, establishing motion tracking at scale as a practical foundation for humanoid control.<\/jats:p>","DOI":"10.1126\/scirobotics.aed4592","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:58:13Z","timestamp":1786557493000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":3,"title":["SONIC: Supersizing motion tracking for natural humanoid whole-body control"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1842-7622","authenticated-orcid":true,"given":"Zhengyi","family":"Luo","sequence":"first","affiliation":[{"name":"NVIDIA, Santa Clara, CA, 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