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Evolution of humanoid locomotion control.

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Τίτλος: Evolution of humanoid locomotion control.
Συγγραφείς: Gu, Yan, Shi, Guanya, Shi, Fan, Chang, I-Chia, Wang, Yen-Jen, Cheng, Qilong, Olkin, Zachary, Lopez-Sanchez, Ivan, Feng, Yunchu, Zhang, Jian, Ames, Aaron D., Su, Hao, Sreenath, Koushil
Πηγή: Science Robotics; 8/12/2026, Vol. 11 Issue 117, p1-19, 19p
Θεματικοί όροι: Robotics, Adaptive control systems, Reinforcement learning, Computer simulation, Human-robot interaction, Probabilistic generative models, Human locomotion
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of Science Robotics is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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  Data: Science Robotics; 8/12/2026, Vol. 11 Issue 117, p1-19, 19p
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science Robotics is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=197075319
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        Value: 10.1126/scirobotics.aed3973
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Reinforcement learning
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      – SubjectFull: Computer simulation
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      – SubjectFull: Probabilistic generative models
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      – SubjectFull: Human locomotion
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              Text: 8/12/2026
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              Y: 2026
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