A Large Model-based Embodied Intelligent Robotic Arm System

With the rapid advancement of artificial intelligence, particularly large models, robotic arms have evolved beyond executing pre-programmed tasks. They can now learn and adapt to new functions through close interaction with their environment, demonstrating capabilities that surpass traditional disembodied intelligence. This paper explores recent developments in large-- model-driven robotic arms with embodied intelligence and their potential applications. The study first outlines the development history of large models and embodied intelligence, followed by an overview of the structure and working principles of embodied intelligent robotic arms. It then examines their ability to integrate multiple sensory modalities (e.g., vision, hearing) for comprehensive environmental understanding and the processing of complex tasks. Further analysis covers multimodal perception, autonomous decision-making, path planning, motion control, and human-- computer interaction feedback mechanisms, highlighting key findings and implementation methods. A concise overview of current research efforts in multimodal perception is essential. This includes the development of algorithms for autonomous decision-making, improvements in path planning and motion control, and the implementation of human-computer interaction feedback mechanisms. Additionally, a detailed analysis of the associated research methods is necessary. A synopsis of key advancements in these domains and their implications for large models and robotic arm technology is essential. Additionally, a discussion of current research challenges is required, including limitations in environment perception and bottlenecks in inference speed. Ultimately, it is essential to consider the future implications of these advancements, which have the potential to revolutionize various fields, including industry, healthcare, and services.