MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning
Min Zhang, Xian Fu, Jianye Hao, Peilong Han, Hao Zhang, Lei Shi,, Hongyao Tang, Yan Zheng

TL;DR
This paper introduces MFE-ETP, a comprehensive benchmark for evaluating multi-modal foundation models on embodied task planning, revealing their current limitations compared to human performance.
Contribution
The work develops a systematic evaluation framework, proposes a challenging benchmark, and provides an automated platform for assessing MFMs in embodied task planning.
Findings
State-of-the-art MFMs perform significantly below human levels.
The benchmark includes diverse, complex, and variable task scenarios.
The evaluation platform enables automated testing of multiple models.
Abstract
In recent years, Multi-modal Foundation Models (MFMs) and Embodied Artificial Intelligence (EAI) have been advancing side by side at an unprecedented pace. The integration of the two has garnered significant attention from the AI research community. In this work, we attempt to provide an in-depth and comprehensive evaluation of the performance of MFM s on embodied task planning, aiming to shed light on their capabilities and limitations in this domain. To this end, based on the characteristics of embodied task planning, we first develop a systematic evaluation framework, which encapsulates four crucial capabilities of MFMs: object understanding, spatio-temporal perception, task understanding, and embodied reasoning. Following this, we propose a new benchmark, named MFE-ETP, characterized its complex and variable task scenarios, typical yet diverse task types, task instances of varying…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Semantic Web and Ontologies · AI-based Problem Solving and Planning
MethodsSoftmax · Attention Is All You Need
