Episode
Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
- Podcast
- Daily Paper Cast
- Published
- Jun 4, 2026
- Duration seconds
- 1540
- Processing state
not_requested- Canonical source
- https://share.transistor.fm/s/7e729012
Actions
POST https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/humanoid-gpt-scaling-data-and-structure-for-zero-shot-motion-tracking/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/daily-paper-cast-7079649/humanoid-gpt-scaling-data-and-structure-for-zero-shot-motion-tracking.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
🤗 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.