ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models

Abstract

Recent advancements in Large Language Models (LLMs) have catalyzed numerous efforts to apply these technologies to embodied tasks, with a particular focus on high-level task planning and task decomposition. LLMs face challenges in understanding the physical world, especially regarding spatial, temporal, and causal relationships among objects and actions. Moreover, the current benchmarks for evaluating these relationships are limited. To further investigate this domain, we introduce a novel embodied task planning benchmark, ET-Plan-Bench. This benchmark features a controllable and diverse array of embodied tasks, varying in levels of difficulty and complexity. It is designed to evaluate two critical dimensions of LLMs’ application in embodied task understanding: spatial understanding (including relation constraints and occlusion of target objects) and temporal and causal comprehension of sequences of actions within an environment. Utilizing multi-source simulators as the backend simulator, ET-Plan-Bench provides immediate environmental feedback to LLMs, enabling dynamic interaction with the environment and the capacity for re-planning as necessary. We evaluated state-of-the-art open-source and closed-source foundational models, including GPT-4, Llama, and Mistral, using our proposed benchmark. While these models perform adequately on simple navigation tasks, their performance significantly deteriorates when con-fronted with tasks that demand a deeper understanding of spatial, temporal, and causal relationships. Consequently, our benchmark distinguishes itself as a large-scale, quantifiable, highly automated, and fine-grained diagnostic framework that presents a substantial challenge to the latest foundational models. We hope it will inspire and propel further research in embodied task planning utilizing foundational models. Code available at: https://github.com/ET-Plan-Bench/ET-Plan-Bench