清华、北信科、复旦团队解读具身智能！大语言模型与世界模型如何让机器人懂物理、会思考？

Core Insights - The article discusses the advancements in embodied AI, particularly the integration of large language models (LLMs) and world models (WMs) to achieve human-like understanding and interaction in physical environments [1][22]. Understanding Embodied Intelligence - Embodied intelligence differs from traditional AI as it actively interacts with the physical world, utilizing sensors for perception, cognitive systems for processing experiences, and actuators for actions, forming a closed loop of perception, cognition, and interaction [2][4]. - The ultimate goal of embodied intelligence is to approach human-level general intelligence, enabling robots to adapt autonomously in dynamic and uncertain environments [4]. Transition from Unimodal to Multimodal - Early embodied intelligence systems relied on single modalities, leading to limitations in performance [5][7]. - The shift to multimodal systems integrates various sensory inputs (visual, auditory, tactile) to enhance task handling capabilities, allowing robots to perform complex tasks more flexibly [8][9]. Core Technologies: LLMs and WMs - LLMs provide semantic understanding, enabling robots to comprehend and plan tasks based on human language, while WMs simulate physical environments to predict outcomes of actions [9][10]. - The combination of LLMs and WMs addresses the shortcomings of each technology, facilitating a more comprehensive approach to embodied intelligence [12][19]. Applications of Embodied Intelligence - In service robotics, modern robots can understand complex instructions and adapt their actions in real-time, improving efficiency and user interaction [20]. - In industrial settings, robots can switch tasks without reprogramming, thanks to the integration of LLMs and WMs, enhancing operational flexibility [20]. Future Challenges - Embodied intelligence requires extensive human-labeled data for training and must evolve towards autonomous learning and exploration in new environments [21]. - Hardware advancements are necessary to support real-time processing of multimodal data, emphasizing the need for efficient chips and low-latency sensors [21]. - Safety and interpretability are critical as robots interact directly with humans, necessitating traceable actions and adherence to ethical standards [21].