{"title":"协调机制、感知和控制:使人形机器人具有具身智能","authors":"Jiahang Huang, Junyao Gao, Zhangguo Yu","doi":"10.1016/j.ipm.2025.104363","DOIUrl":null,"url":null,"abstract":"<div><div>Humanoid robotics has evolved from early bipedal locomotion studies to modern systems integrating neuromorphic intelligence. This review systematically examines nearly 300 research studies, identifying key advancements in biomechanical optimization, multimodal perception, motion intelligence, and intelligent interaction. Recent progress in biomechanical optimization through material-structure co-design has led to lighter, more adaptive robotic frameworks, improving energy efficiency, compliance, and mechanical robustness. Meanwhile, multimodal perception has significantly enhanced environmental understanding by integrating vision, force, and proprioceptive sensing, enabling robust scene interpretation and adaptive interaction. However, challenges remain in real-time sensor fusion and uncertainty handling, limiting performance in dynamic and unstructured environments. Advancements in motion intelligence are increasingly driven by frameworks that integrate model-based control with learning-driven adaptation, allowing humanoid robots to achieve greater efficiency, agility, and generalizability in motion planning and execution. At the same time, intelligent interaction has evolved with approaches such as imitation learning, shared control, brain-computer interfaces, teleoperation, and large models, strengthening the link between perception and action for seamless human-robot collaboration. While these innovations enhance adaptability and interaction efficiency, robustness in intent-driven decision-making and real-world deployment remains a key challenge. Commercialization efforts have accelerated the transition from laboratory prototypes to practical applications, particularly in industrial automation and assistive robotics. However, scalability, autonomy, and safety remain critical concerns, requiring further advancements in hardware efficiency, neuromorphic computing, and AI-driven architectures. By synthesizing theoretical insights with recent technological developments, this review provides a structured roadmap for advancing humanoid robotics toward real-world implementation.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 1","pages":"Article 104363"},"PeriodicalIF":6.9000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Orchestrating mechanics, perception and control: Enabling embodied intelligence in humanoid robots\",\"authors\":\"Jiahang Huang, Junyao Gao, Zhangguo Yu\",\"doi\":\"10.1016/j.ipm.2025.104363\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Humanoid robotics has evolved from early bipedal locomotion studies to modern systems integrating neuromorphic intelligence. This review systematically examines nearly 300 research studies, identifying key advancements in biomechanical optimization, multimodal perception, motion intelligence, and intelligent interaction. Recent progress in biomechanical optimization through material-structure co-design has led to lighter, more adaptive robotic frameworks, improving energy efficiency, compliance, and mechanical robustness. Meanwhile, multimodal perception has significantly enhanced environmental understanding by integrating vision, force, and proprioceptive sensing, enabling robust scene interpretation and adaptive interaction. However, challenges remain in real-time sensor fusion and uncertainty handling, limiting performance in dynamic and unstructured environments. Advancements in motion intelligence are increasingly driven by frameworks that integrate model-based control with learning-driven adaptation, allowing humanoid robots to achieve greater efficiency, agility, and generalizability in motion planning and execution. At the same time, intelligent interaction has evolved with approaches such as imitation learning, shared control, brain-computer interfaces, teleoperation, and large models, strengthening the link between perception and action for seamless human-robot collaboration. While these innovations enhance adaptability and interaction efficiency, robustness in intent-driven decision-making and real-world deployment remains a key challenge. Commercialization efforts have accelerated the transition from laboratory prototypes to practical applications, particularly in industrial automation and assistive robotics. However, scalability, autonomy, and safety remain critical concerns, requiring further advancements in hardware efficiency, neuromorphic computing, and AI-driven architectures. By synthesizing theoretical insights with recent technological developments, this review provides a structured roadmap for advancing humanoid robotics toward real-world implementation.</div></div>\",\"PeriodicalId\":50365,\"journal\":{\"name\":\"Information Processing & Management\",\"volume\":\"63 1\",\"pages\":\"Article 104363\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing & Management\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306457325003048\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457325003048","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Orchestrating mechanics, perception and control: Enabling embodied intelligence in humanoid robots
Humanoid robotics has evolved from early bipedal locomotion studies to modern systems integrating neuromorphic intelligence. This review systematically examines nearly 300 research studies, identifying key advancements in biomechanical optimization, multimodal perception, motion intelligence, and intelligent interaction. Recent progress in biomechanical optimization through material-structure co-design has led to lighter, more adaptive robotic frameworks, improving energy efficiency, compliance, and mechanical robustness. Meanwhile, multimodal perception has significantly enhanced environmental understanding by integrating vision, force, and proprioceptive sensing, enabling robust scene interpretation and adaptive interaction. However, challenges remain in real-time sensor fusion and uncertainty handling, limiting performance in dynamic and unstructured environments. Advancements in motion intelligence are increasingly driven by frameworks that integrate model-based control with learning-driven adaptation, allowing humanoid robots to achieve greater efficiency, agility, and generalizability in motion planning and execution. At the same time, intelligent interaction has evolved with approaches such as imitation learning, shared control, brain-computer interfaces, teleoperation, and large models, strengthening the link between perception and action for seamless human-robot collaboration. While these innovations enhance adaptability and interaction efficiency, robustness in intent-driven decision-making and real-world deployment remains a key challenge. Commercialization efforts have accelerated the transition from laboratory prototypes to practical applications, particularly in industrial automation and assistive robotics. However, scalability, autonomy, and safety remain critical concerns, requiring further advancements in hardware efficiency, neuromorphic computing, and AI-driven architectures. By synthesizing theoretical insights with recent technological developments, this review provides a structured roadmap for advancing humanoid robotics toward real-world implementation.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.