Research on deep learning analysis and optimization of humanoid robot based on Yushu Technology

<div>Humanoid robots, as core carriers of embodied intelligence, rely on their deep learning and behavior prediction capabilities to break through the bottleneck in general-task execution. Taking Unitree as a case study, this research conducts an in-depth analysis of the current technical status, challenges, and optimization paths of humanoid robots in this field. A dynamic environment perception-decision-execution closed-loop system is constructed, encompassing a multimodal perception layer, a hybrid decision-making layer, and a realtime execution layer. It is proposed that hardware iteration must be deeply coordinated with AI algorithms. In terms of model optimization, a multi-task lightweight model architecture is established, which innovatively combines dynamic environment adaptation algorithms with transfer learning mechanisms. Meanwhile, efforts are being made to develop a native multimodal industry-specific large-scale model for robots, exploring the engineering</div> <div>implementation plan for humanoid robot behavior prediction. Experimental verification not only tests the performance of Unitree’s humanoid robots but also identifies technical bottlenecks such as insufficient chip computing power, lack of industry-specific large-scale models, and dependence on remote control, along with targeted optimization suggestions. Finally, this study looks ahead to the development trends of humanoid robot technology, including breakthroughs in general AI models, the implementation of neuromorphic computing, and aspects of social impact and ethical reconstruction, aiming to promote the development of the humanoid robot industry and expand its applications in diverse scenarios such as industry and households.</div>