LAMARS: Large Language Model-Based Anticipation Mechanism Acceleration in Real-Time Robotic Systems

Abstract

Large language models (LLMs) have assumed an increasingly crucial role in robotic systems because of their ability to leverage the extensive knowledge they possess in robotic inference and task handling. Although LLMs offer significant potential, their integration into robotic systems poses substantial challenges, particularly with regard to computational efficiency and latency. To address this challenge, this study presents LAMARS, an LLM-based anticipation mechanism designed to accelerate real-time robotic systems. LAMARS leverages the predictive power and zero-shot capabilities of LLMs combined with an anticipation mechanism and vision-language processing to position a robot in advance for upcoming tasks. This reduces latency and optimizes path planning without requiring expensive training data. Our evaluations in a realistic simulation environment and with a variation of the RLBench dataset demonstrated that LAMARS achieved an average success rate of 0.79 and improves efficiency by up to 52.4% compared to existing methods, significantly lowering path planning costs. These results indicate that LAMARS effectively accelerates directive execution, making it a promising solution to minimize delays in real-time robotic systems.

Keywords

• Human-robot interaction
• large language models
• latency reduction
• vision-language models
• path planning