在人工智能领域，实现通用人工智能（AGI）一直是科研人员追求的目标。具身智能作为实现这一目标的必经之路，近年来吸引了全球科技和产业界的广泛关注。通过智能体与数字空间和物理世界的交互，完成复杂任务，具身智能旨在构建出能够理解、适应并影响真实世界环境的智能系统。近期，由鹏城实验室多智能体与具身智能研究所与中山大学 HCP 实验室联合开展的研究，推出了一篇全球首篇具身智能综述，全面解析了这一领域的最新进展。
这篇综述基于对近400篇文献的深入调研，从多个维度全面剖析了具身智能的研究现状。首先，它介绍了具有代表性的具身机器人和具身仿真平台，探讨了它们的研究重点和局限性。随后，该综述详细解析了具身智能的四个主要研究内容：具身感知、具身交互、具身智能体和虚拟到现实的迁移。这四个领域涵盖了最先进的方法、基本范式和全面的数据集，展示了在复杂环境中，具身智能体相较于传统深度强化学习方法的多样性和泛化能力。
### 具身智能的前世今生
具身智能的概念源于艾伦·图灵在1950年提出的具身图灵测试，旨在验证智能体是否能展现出对真实世界环境的适应性与理解能力。随着计算机视觉、自然语言处理和机器人技术的快速发展，具身智能已成为实现通用人工智能的关键途径。它不仅要求智能体理解并执行人类的语言指令，还要求其在动态的数字和物理环境中主动进行探索和交互。
### 最新进展与挑战
在具身感知方面，通过视觉编码器预训练的视觉表示，智能体能够对物体的类别、姿态和几何形状进行精确估计，全面感知复杂环境。强大的大语言模型则使机器人能够更好地理解人类的复杂指令，并通过视觉和语言表示的对齐，实现任务的有效执行。
世界模型技术展示了智能体对物理定律的理解能力，使它们能够预测和模拟环境变化，从而在虚拟环境中学习，最终实现虚拟到现实的有效迁移。然而，具身智能的发展仍面临诸多挑战，包括如何在不确定和动态的环境中进行高效学习，以及如何在物理世界中实现精准的交互与控制。
### 结论与展望
该综述不仅为具身智能研究提供了基础性的参考，还指出了该领域未来的发展方向，包括提高智能体在复杂环境下的适应性和学习效率，以及加强跨领域技术的整合与应用。随着技术的不断进步和应用场景的不断扩展，具身智能有望在未来实现更广泛、更深入的应用，为人类创造更加智能、安全和高效的生活环境。
### 全文发布与资源获取
为了促进学术交流与传播，该综述已在全球范围内发布，并在 Github 上发布了具身智能论文列表，方便研究者和爱好者追踪最新进展和相关资源。论文和代码仓库的持续更新，将进一步推动具身智能技术的创新与发展。
### 结语
作为全球首篇深入解析具身智能的综述，本研究不仅展示了具身智能领域的最新成果，也为未来的研究和应用指明了方向。随着人工智能技术的不断演进，具身智能有望在实现通用人工智能的道路上迈出关键一步，为人类社会带来革命性的变革。
英语如下：
### Global First: Comprehensive Review of Embodied Intelligence, Unveiling a New Era
In the realm of artificial intelligence (AI), the pursuit of general artificial intelligence (AGI) has been a long-standing goal of researchers. Embodied intelligence, serving as a necessary pathway to AGI, has recently garnered significant attention from the global technology and industry sectors. By enabling intelligent agents to interact with digital and physical worlds, completing complex tasks, embodied intelligence aims to construct intelligent systems capable of understanding, adapting, and influencing real-world environments. A joint study conducted by the Multi-Agent and Embodied Intelligence Institute at the鹏城实验室 and the HCP Laboratory at Sun Yat-sen University, has released the world’s first comprehensive review on embodied intelligence, providing an in-depth analysis of the latest advancements in this field.
Based on an in-depth review of nearly 400 scholarly articles, this review covers various dimensions to dissect the current state of embodied intelligence research. It introduces representative embodied robots and simulation platforms, exploring their research priorities and limitations. Subsequently, the review delves into the four main areas of embodied intelligence: embodied perception, embodied interaction, embodied agents, and the migration from virtual to reality. These areas encompass the most advanced methods, foundational paradigms, and comprehensive datasets, showcasing the diversity and generalization capabilities of embodied intelligent agents compared to traditional deep reinforcement learning approaches.
### The Past, Present, and Future of Embodied Intelligence
The concept of embodied intelligence originated from Alan Turing’s embodied Turing test proposed in 1950, which aimed to validate the adaptability and understanding capabilities of intelligent agents in real-world environments. With the rapid advancement of computer vision, natural language processing, and robotics technologies, embodied intelligence has become a crucial avenue for achieving general AI. It requires not only the ability of intelligent agents to understand and execute human language commands but also their active exploration and interaction in dynamic digital and physical environments.
### Recent Progress and Challenges
In the realm of embodied perception, through pre-trained visual encoders, intelligent agents can accurately estimate object categories, poses, and geometrical shapes, enabling comprehensive environmental perception. Powerful large language models facilitate robots in better understanding complex human instructions and executing tasks through the alignment of visual and linguistic representations.
World models demonstrate the understanding of physical laws in intelligent agents, enabling them to predict and simulate environmental changes, thereby learning in virtual environments and achieving effective migration to the real world. However, the development of embodied intelligence still faces numerous challenges, including efficient learning in uncertain and dynamic environments, and precise interaction and control in the physical world.
### Conclusion and Outlook
This review not only provides a foundational reference for embodied intelligence research but also points out future directions in the field, including enhancing the adaptability and learning efficiency of intelligent agents in complex environments and strengthening the integration and application of interdisciplinary technologies. With the continuous advancement of technology and the expansion of application scenarios, embodied intelligence is poised to achieve broader and deeper applications, creating smarter, safer, and more efficient living environments for humanity.
### Full Release and Resource Access
To facilitate academic exchange and dissemination, this review has been released globally, and a list of embodied intelligence papers is available on Github, enabling researchers and enthusiasts to track the latest developments and related resources. The continuous updates to the papers and code repositories will further drive innovation and development in embodied intelligence technology.
### Closing Remarks
As the world’s first comprehensive review on embodied intelligence, this study not only showcases the latest achievements in the field but also illuminates the future directions for research and applications. With the continuous evolution of AI technology, embodied intelligence is expected to make a pivotal step towards achieving general AI, bringing about revolutionary changes to human society.
【来源】https://www.jiqizhixin.com/articles/2024-07-26-6
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