# Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/humanoid-gpt-scaling-data-and-structure-for-zero-shot-motion-tracking
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/humanoid-gpt-scaling-data-and-structure-for-zero-shot-motion-tracking.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-06-04T03:56:18+00:00
Episode link: https://share.transistor.fm/s/7e729012
Audio file: https://media.transistor.fm/7e729012/7ef3297e.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/humanoid-gpt-scaling-data-and-structure-for-zero-shot-motion-tracking
Duration seconds: 1540

## Resource

🤗 Upvotes: 33 | cs.RO, cs.AI, cs.CV Authors: Zekun Qi, Xuchuan Chen, Dairu Liu, Chenghuai Lin, Yunrui Lian, Sikai Liang, Zhikai Zhang, Yu Guan, Jilong Wang, Wenyao Zhang, Xinqiang Yu, He Wang, Li Yi Title: Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking Arxiv: http://arxiv.org/abs/2606.03985v1 Abstract: We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrained by scarce data and an agility-generalization trade-off, Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings. Scaling both data and model capacity yields a single generative Transformer that tracks highly dynamic behaviors while achieving unprecedented zero-shot generalization to unseen motions and control tasks. Extensive experiments and scaling analyses show that our model establishes a new performance frontier, demonstrating robust zero-shot generalization to unseen tasks while simultaneously tracking highly dynamic and complex motions.

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