对具身人工智能的倡议
A call for embodied AI
AI总结：
该研究提出具身人工智能是迈向通用人工智能的下一个关键阶段，梳理具身概念的跨领域演变，构建基于认知架构、契合主动推理原理的EAI理论框架，指明当前挑战并为未来EAI研究提供基础指引。
摘要：
我们提出具身人工智能（Embodied AI，简称EAI）是追求通用人工智能（Artificial General Intelligence，简称AGI）进程中的下一个基础性阶段，并将其与当前的AI进展，尤其是大语言模型（Large Language Models）进行对比。我们梳理了具身概念在哲学、心理学、神经科学和机器人学等不同领域的演变，以凸显EAI与经典静态学习范式的区别。通过拓宽具身人工智能的研究范畴，我们引入了一个基于认知架构的理论框架，强调感知、行动、记忆和学习是具身智能体的核心组成部分。该框架与Friston的主动推理原理相一致，为EAI的发展提供了一套综合性方法。尽管AI领域已取得诸多进展，但仍存在诸多重大挑战，比如新型AI学习理论的构建以及先进硬件的创新。我们的讨论为未来的具身人工智能研究奠定了基础性指导方针。我们强调了打造能够在现实世界环境中与人类及其他智能实体无缝沟通、协作和共存的具身人工智能智能体的重要性，旨在引导AI社区应对在追求AGI的道路上面临的多方面挑战并把握机遇。
英文摘要：
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
