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A Comprehensive Survey on Embodied Intelligence: Advancements, Challenges, and Future Perspectives

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Abstract & Overview

Embodied Intelligence, which integrates physical interaction capabilities with cognitive computation in real-world scenarios, provides a promising path to achieve Artificial General Intelligence (AGI). Recently, the landscape of embodied intelligence has grown profoundly, empowering robotics, autonomous driving, intelligent manufacturing, and so on. This paper presents a comprehensive survey on the evolution of embodied intelligence, tracing its journey from philosophical roots to contemporary advancements. We emphasize significant progress in the integration of perceptual, cognitive, and behavioral components, rather than focusing on these elements in isolation. Despite these advancements, several challenges remain, including hardware limitations, model generalization, physical world understanding, multimodal integration, and ethical considerations, which are critical for the development of robust and reliable embodied intelligence systems. To address these challenges, we outline future research directions, emphasizing Large Perception-Cognition-Behavior (PCB) models, physical intelligence, and morphological intelligence. Central to these perspectives is the general agent framework termed as Bcent, which integrates perception, cognition, and behavior dynamics. Bcent aims to enhance the adaptability, robustness, and intelligence of embodied systems, aligning with the ongoing progress in robotics, autonomous systems, healthcare, and more.

Cite this record

Fuchun Sun, Runfa Chen, Tianying Ji, Yu Luo, Huaidong Zhou, Huaping Liu (2024). A Comprehensive Survey on Embodied Intelligence: Advancements, Challenges, and Future Perspectives. CAAI Artificial Intelligence Research, 9150042–9150042. https://doi.org/10.26599/air.2024.9150042

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Sun, F., Chen, R., Ji, T., Luo, Y., Zhou, H., & Liu, H. (2024). A Comprehensive Survey on Embodied Intelligence: Advancements, Challenges, and Future Perspectives. CAAI Artificial Intelligence Research, 9150042–9150042. https://doi.org/10.26599/air.2024.9150042
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