Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Training one AI model on billions of motion frames to control robot bodies
Researchers built Humanoid-GPT, a single AI model trained on 2 billion frames of human motion data that can control a humanoid robot to perform movements it has never seen before. Unlike earlier systems that required separate training for each new motion, this model generalizes to entirely new behaviors and tasks without additional fine-tuning, while also handling complex, fast-moving actions.
Humanoid robots currently require time-consuming, task-specific training to learn new movements. A model that can instantly adapt to unseen motions could dramatically speed up robot deployment in factories, hospitals, and other real-world settings. This approach shows that scaling up both training data and model size—similar to how large language models work—may be the path to robots that are genuinely flexible rather than narrowly specialized.