Humanoid World Models: Open World Foundation Models for Humanoid Robotics

Humanoid robots have the potential to perform complex tasks in human centered environments but require robust predictive models to reason about the outcomes of their actions. We introduce Humanoid World Models (HWM) a family of lightweight open source video based models that forecast future egocentric observations conditioned on actions. We train two types of generative models Masked Transformers and FlowMatching on 100 hours of humanoid demonstrations. Additionally we explore architectural variants with different attention mechanisms and parameter sharing strategies. Our parameter sharing techniques reduce model size by 33 to 53 with minimal impact on performance or visual fidelity. HWM is designed to be trained and deployed in practical academic and small lab settings such as 1 to 2 GPUs.

@article{ali2025_2506.01182,
  title={ Humanoid World Models: Open World Foundation Models for Humanoid Robotics },
  author={ Muhammad Qasim Ali and Aditya Sridhar and Shahbuland Matiana and Alex Wong and Mohammad Al-Sharman },
  journal={arXiv preprint arXiv:2506.01182},
  year={ 2025 }
}