Authors
Yan Gu, Guanya Shi, Fan Shi, I-Chia Chang, Yen-Jen Wang, Qilong Cheng, Zachary Olkin, Ivan Lopez-Sanchez, Yunchu Feng, Jian Zhang, Aaron D Ames, Hao Su, Koushil Sreenath
Published in
Science robotics. Volume 11. Issue 117. Pages eaed3973. Aug 19, 2026. Epub Aug 19, 2026.
Abstract
Humanoid robots stand at the forefront of robotics, aiming to capture the agility, robustness, and expressivity of human movement in an anthropomorphic form. The locomotion control of humanoids has evolved from classical model-based methods to reinforcement learning powered by large-scale simulation and now to generative models that produce adaptive, whole-body behaviors, propelling humanoids toward operation in real-world environments. This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning. We identify three core principles linking these paradigms: physics-based modeling, constrained decision-making, and adaptation to uncertainty. Building on these connections, we provide recommendations for researchers and outline open challenges in safety, accessibility, and human-level capability. These directions represent a transformation from engineered stability to intelligent autonomy, laying the groundwork for humanoid generalists capable of operating safely, collaborating naturally, and extending human capability in the open world.
PMID:
42616832
Bibliographic data and abstract were imported from PubMed on 20 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 50
- Comments 0