دانلود کتاب هوش مصنوعی تجسمی خودمختار به سوی هوش خود-تکاملی

Embodied Artificial Intelligence (AI) has emerged as a cornerstone in advancing intelligent systems, highlighting that cognition and reasoning cannot be decoupled from perception and interaction with the physical world. Unlike purely digital intelligence, embodied AI enables agents to ground abstract reasoning in real-world contexts, thereby bridging the gap between virtual and physical worlds. As large-scale AI models have demonstrated unprecedented reasoning ability, this embodied paradigm provides a natural pathway for extending intelligence to dynamic, uncertain, and open environments, which is critical for realizing Artificial General Intelligence (AGI). This book, entitled Autonomous Embodied AI: Towards Self-evolving Intelligence, authored by Xin Wang, Tongtong Feng, Huaping Liu, and Wenwu Zhu, provides a comprehensive and forward-looking perspective on this emerging field. Part I establishes the foundation by presenting key technologies, components, and architectures of embodied AI, ranging from large language model (LLM)-based, world model-based to joint LLM and world model-based embodied AI. It also explores autonomous embodied AI and autonomous swarm embodied AI, offering a clear progression from individual autonomy to swarm intelligence. Part II shifts the discussion to self-evolving embodied AI, identifying two central pillars: self-evolving world models that construct predictive representations of the environment, and selfevolving agents that operationalize adaptation and improvement in a real-time manner. This dual framework provides both the conceptual depth and practical guidance for advancing toward agents that can continuously learn, self-reflect, and evolve. Such a vision not only advances theoretical understanding but also provides practical guidelines for the future development of embodied AI. The authors, renowned experts in embodied AI, autonomous learning, and swarm intelligence, have successfully combined rigorous research insights with practical applications. Their work balances conceptual clarity with technical details, making the book equally valuable for researchers seeking a broad overview and practitioners aiming for practical methodologies. By integrating foundations, stateof- the-art methods, and forward-looking visions, the book offers a unique blueprint for building the next generation of embodied AI. This volume is a timely contribution to the AI community. It will serve as an indispensable reference for scholars, engineers, and graduate students who wish to explore embodied AI. More importantly, it marks a significant step in the journey toward self-evolving embodied AI, underscoring the importance of bridging digital intelligence with the physical world. I strongly recommend this book to those eager to shape and participate in the future of embodied AI.