LSAI: A Large Small AI Model Codesign Framework for Agentic Robot Scenarios

TL;DR

The paper introduces LSAI, a framework combining large and small AI models for agentic robots, enhancing real-time cooperation, environment sensing, and resource efficiency in complex scenarios.

Contribution

It proposes a novel attention-based model aggregation and adaptive model splitting algorithm for improved robot cooperation and resource management.

Findings

Achieves up to 20.4% higher sensing accuracy

Reduces sensing cooperation latency by 17.9%

Demonstrates effectiveness through simulation results

Abstract

The development of Artificial Intelligence (AI) has enabled agentic robots an appealing paradigm for various applications, such as research and rescue in complex environment. In this context, the next wireless communication technology facilitates robot cooperation for efficient environment sensing and exploration. However, traditional AI solutions cannot always provide reasonable resource utilization decisions, which makes it challenging to achieve both accurate and low-latency research and rescue. To address this issue, we propose a, LSAI, a large small AI model codesign framework to achieve highly accurate and real-time robot cooperation with deep interaction between large AI model and small AI model. We first propose an attention-based model aggregation for LAI construction. It can assist agentic robots in accurately sensing physical environments. Next, we design an adaptive model splitting and update algorithm to enable the robots to perform accurate path planning for high-efficiency environment sensing with low energy consumption. Finally, we demonstrate the effectiveness of our proposed LSAI framework. The simulation results indicate that our solution achieves sensing accuracy of up to 20.4% while reducing sensing cooperation latency by an average of 17.9% compared to traditional AI solutions.