As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social syste...As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.展开更多
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap fr...Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.展开更多
Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in e...Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other fields.From the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at hand.These tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target tasks.This limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.展开更多
The rise of embodied artificial intelligence(embodied AI)marks a pivotal shift in AI,moving it from the digital realm into the physical world.This transition aims to create autonomous robots capable of perceiving,reas...The rise of embodied artificial intelligence(embodied AI)marks a pivotal shift in AI,moving it from the digital realm into the physical world.This transition aims to create autonomous robots capable of perceiving,reasoning,and acting in complex unstructured environments.Achieving this goal demands unprecedented capabilities for robots to comprehensively perceive both their external surroundings and internal states.However,traditional sensors cannot meet the requirement of robotic perception systems due to limitations in size and power consumption.In this context,micro-electromechanical system(MEMS)technology emerges as a critical enabler for advancing next-generation robotic perception capabilities.Its core advantages,including miniaturization,low power consumption,high integration,and cost-effectiveness,make it ideal for this role.This review provides a comprehensive overview of the latest advancements in MEMS sensing technologies specifically designed for embodied AI robots.By integrating diverse MEMS sensors,such as those for ranging,inertia,tactile,hearing,and olfaction,robots can achieve rich multimodal perception.These highly integrated sensing systems provide a robust technological foundation for robot applications in various fields,demonstrating the immense potential of MEMS technology in promoting autonomy,safety,and interactive capabilities in robots.In essence,the future of embodied AI will be built upon a powerful symbiosis:MEMS providing the rich semantic-aware'sensory neurons'and AI models providing the'cognitive brain'.This fusion promises to usher in an era of truly perceptive and intelligent machines.展开更多
A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Googl...A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Google’s AlphaGo defeated Go world champion Lee Se-dol and gained widespread public attention with the release of OpenAI's ChatGPT in November 2022.Just one year after ChatGPT’s debut,Chinese Al firm DeepSeek launched its open-source general large model,a milestone in the evolution of Al technology.展开更多
Creating intelligent beings like humans is a long-standing goal in AI research, such as intelligent robots in science fiction.Classic AI technologies are disembodied, and insufficient to make robots intelligently beha...Creating intelligent beings like humans is a long-standing goal in AI research, such as intelligent robots in science fiction.Classic AI technologies are disembodied, and insufficient to make robots intelligently behave in the real world. In contrast,embodied artificial intelligence (Embodied AI) enables artificial agents with physical embodiment to achieve intelligentbehavior through interactions with environments. However, there are few comprehensive surveys on Embodied AI from theperspective of robot behavior within the AI domain. Thus, we provide a comprehensive survey on Embodied AI. According tothe process of robot behavior, we categorize Embodied AI into three modules: embodied perception, embodied decision-making,and embodied execution. For each module, we review its tasks, methods, and challenges. We hope this survey can provide astructural framework for Embodied AI research. Besides, we also pay attention to large foundation models in Embodied AI.展开更多
1Introduction Embodied Artificial Intelligence(Embodied AI)has recently become a key research focus[1].It emphasizes agents'abilities to perceive,comprehend,and act in physical worlds to complete tasks.Simulation ...1Introduction Embodied Artificial Intelligence(Embodied AI)has recently become a key research focus[1].It emphasizes agents'abilities to perceive,comprehend,and act in physical worlds to complete tasks.Simulation platforms are essential in this area,as they simulate agent behaviors in set environments and tasks,thereby accelerating algorithm validation and optimization.However,constructing such a platform presents several challenges.展开更多
The realization of natural and authentic facial expressions in humanoid robots poses a challenging and prominent research domain,encompassing interdisciplinary facets including mechanical design,sensing and actuation ...The realization of natural and authentic facial expressions in humanoid robots poses a challenging and prominent research domain,encompassing interdisciplinary facets including mechanical design,sensing and actuation control,psychology,cognitive science,flexible electronics,artificial intelligence(AI),etc.We have traced the recent developments of humanoid robot heads for facial expressions,discussed major challenges in embodied AI and flexible electronics for facial expression recognition and generation,and highlighted future trends in this field.Developing humanoid robot heads with natural and authentic facial expressions demands collaboration in interdisciplinary fields such as multi-modal sensing,emotional computing,and human-robot interactions(HRIs)to advance the emotional anthropomorphism of humanoid robots,bridging the gap between humanoid robots and human beings and enabling seamless HRIs.展开更多
基金supported by the National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005,U2441245,62173141)+3 种基金CNPC Innovation Found(2024DQ02-0507)Shanghai Natural Science(24ZR1416400)Shanghai Baiyu Lan Talent Program Pujiang Project(24PJD020)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017)
文摘As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.
基金supported in part by NSFC under Grant 62422407in part by RGC under Grant 26204424in part by ACCESS–AI Chip Center for Emerging Smart Systems, sponsored by the Inno HK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government
文摘Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.
基金supported by the Guangdong Provincial Science and Technology Program(Grant No.2023A0505030003).
文摘Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other fields.From the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at hand.These tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target tasks.This limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.
基金supported by the Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars(Grant BK20220056)the National Natural Science Foundation of China(Grant 52475599).
文摘The rise of embodied artificial intelligence(embodied AI)marks a pivotal shift in AI,moving it from the digital realm into the physical world.This transition aims to create autonomous robots capable of perceiving,reasoning,and acting in complex unstructured environments.Achieving this goal demands unprecedented capabilities for robots to comprehensively perceive both their external surroundings and internal states.However,traditional sensors cannot meet the requirement of robotic perception systems due to limitations in size and power consumption.In this context,micro-electromechanical system(MEMS)technology emerges as a critical enabler for advancing next-generation robotic perception capabilities.Its core advantages,including miniaturization,low power consumption,high integration,and cost-effectiveness,make it ideal for this role.This review provides a comprehensive overview of the latest advancements in MEMS sensing technologies specifically designed for embodied AI robots.By integrating diverse MEMS sensors,such as those for ranging,inertia,tactile,hearing,and olfaction,robots can achieve rich multimodal perception.These highly integrated sensing systems provide a robust technological foundation for robot applications in various fields,demonstrating the immense potential of MEMS technology in promoting autonomy,safety,and interactive capabilities in robots.In essence,the future of embodied AI will be built upon a powerful symbiosis:MEMS providing the rich semantic-aware'sensory neurons'and AI models providing the'cognitive brain'.This fusion promises to usher in an era of truly perceptive and intelligent machines.
文摘A round 2010,academic circles witnessed a surge in Al research fueled by break-throughs such as the ImageNet project,a publicly available large-scale image database.The field reached a tipping point in 2016 when Google’s AlphaGo defeated Go world champion Lee Se-dol and gained widespread public attention with the release of OpenAI's ChatGPT in November 2022.Just one year after ChatGPT’s debut,Chinese Al firm DeepSeek launched its open-source general large model,a milestone in the evolution of Al technology.
文摘Creating intelligent beings like humans is a long-standing goal in AI research, such as intelligent robots in science fiction.Classic AI technologies are disembodied, and insufficient to make robots intelligently behave in the real world. In contrast,embodied artificial intelligence (Embodied AI) enables artificial agents with physical embodiment to achieve intelligentbehavior through interactions with environments. However, there are few comprehensive surveys on Embodied AI from theperspective of robot behavior within the AI domain. Thus, we provide a comprehensive survey on Embodied AI. According tothe process of robot behavior, we categorize Embodied AI into three modules: embodied perception, embodied decision-making,and embodied execution. For each module, we review its tasks, methods, and challenges. We hope this survey can provide astructural framework for Embodied AI research. Besides, we also pay attention to large foundation models in Embodied AI.
基金supported by the National Natural Science Foundation of China(Grant No.62322601).
文摘1Introduction Embodied Artificial Intelligence(Embodied AI)has recently become a key research focus[1].It emphasizes agents'abilities to perceive,comprehend,and act in physical worlds to complete tasks.Simulation platforms are essential in this area,as they simulate agent behaviors in set environments and tasks,thereby accelerating algorithm validation and optimization.However,constructing such a platform presents several challenges.
基金supported by the National Natural Science Foundation of China(nos.52188102 and 51925503)the Science and Technology Development Fund of Macao SAR(file na.0117/2024/AMJ)+1 种基金Zhuhai UM Science&Technology Research Institute(CP-009-2024)the State Key Laboratory of Intelligent Manufacturing Equipment and Tech-nology(IMETKF2024003),HUST,Wuhan,China.
文摘The realization of natural and authentic facial expressions in humanoid robots poses a challenging and prominent research domain,encompassing interdisciplinary facets including mechanical design,sensing and actuation control,psychology,cognitive science,flexible electronics,artificial intelligence(AI),etc.We have traced the recent developments of humanoid robot heads for facial expressions,discussed major challenges in embodied AI and flexible electronics for facial expression recognition and generation,and highlighted future trends in this field.Developing humanoid robot heads with natural and authentic facial expressions demands collaboration in interdisciplinary fields such as multi-modal sensing,emotional computing,and human-robot interactions(HRIs)to advance the emotional anthropomorphism of humanoid robots,bridging the gap between humanoid robots and human beings and enabling seamless HRIs.
