OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
H. Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre Côté, Bang Liu · 2024
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions.Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF.Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components-ranging from visual perception to action execution-on task performance.To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor.Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance.Furthermore, we innovate within this space by deploying a multi-agent LLM communication strategy on a TextWorld counterpart, further enhancing task performance.Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance.1