OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following

• URL: http://arxiv.org/abs/2403.03017v1
• Date: Tue, 5 Mar 2024 14:53:53 GMT
• Title: OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
• Authors: Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre C\^ot\'e, Bang Liu
• Abstract summary: 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 EIF. We introduce OPEx, a comprehensive framework that delineates the core components essential for solving EIF tasks: Observer, Planner, and Executor.
• Score: 38.99303334457817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 dialogue 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.

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