Research on Multi-modal Instruction Mapping Human-Computer Interaction System Integrating Llama2 and ROS

At present, the field of robot-human-computer interaction generally relies on programmatic operation; the mapping accuracy between multi-modal instructions and robot operating system (ROS) is low, there exists insufficient fusion adaptability between Llama2 large language model (Llama2) and ROS, and the real-time response performance is poor, which makes it difficult to meet the human-computer interaction requirements in complex scenarios. This paper designs and implements a multi-modal instruction mapping human-computer interaction system that fuses Llama2 and ROS. The core innovations are as follows: 1. Propose a deep fusion architecture between Llama2 and ROS, build a proprietary adaptation middleware, effectively solve the problems of semantic misalignment and excessive deployment overhead between the two, and transcend the inherent constraints of conventional stitching approaches; 2. Design a multi-modal (speech + vision) adaptive instruction mapping algorithm to realize accurate mapping and automatic conversion from multi-modal instructions to ROS function packages without manual intervention and adaptation; 3. Introduce the model quantization optimization strategy, significantly reduce the deployment memory overhead of the Llama2 model, and improve the real-time performance of instruction response. Experiments show that the instruction mapping accuracy of the system is 98.3%, the average response time is 118ms, and the memory overhead is reduced by 36%; Compared with the traditional method, the accuracy is improved by 11.5 percentage points, the response time is shortened by 47ms, and the memory overhead is reduced by 29 percentage points, which fully verifies the feasibility and superiority of the system.