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&nbsp;execution. Taking Unitree as a case study, this research conducts an in-depth analysis of the&nbsp;current technical status, challenges, and optimization paths of humanoid robots in this field.&nbsp;A dynamic environment perception-decision-execution closed-loop system is constructed,&nbsp;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&nbsp;AI algorithms. In terms of model optimization, a multi-task lightweight model architecture&nbsp;is established, which innovatively combines dynamic environment adaptation algorithms with transfer learning mechanisms. Meanwhile, efforts are being made to develop a native&nbsp;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&rsquo;s humanoid robots but also identifies technical&nbsp;bottlenecks such as insufficient chip computing power, lack of industry-specific large-scale&nbsp;models, and dependence on remote control, along with targeted optimization suggestions.&nbsp;Finally, this study looks ahead to the development trends of humanoid robot technology,&nbsp;including breakthroughs in general AI models, the implementation of neuromorphic&nbsp;computing, and aspects of social impact and ethical reconstruction, aiming to promote the&nbsp;development of the humanoid robot industry and expand its applications in diverse scenarios&nbsp;such as industry and households.</div>