  In this study, we explore the potential of Multimodal Large Language Models
(MLLMs) in improving embodied decision-making processes for agents. While Large
Language Models (LLMs) have been widely used due to their advanced reasoning
skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual
understanding and reasoning capabilities. We investigate whether
state-of-the-art MLLMs can handle embodied decision-making in an end-to-end
manner and whether collaborations between LLMs and MLLMs can enhance
decision-making. To address these questions, we introduce a new benchmark
called PCA-EVAL, which evaluates embodied decision-making from the perspectives
of Perception, Cognition, and Action. Additionally, we propose HOLMES, a
multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs
to gather multimodal information for informed decision-making. We compare
end-to-end embodied decision-making and HOLMES on our benchmark and find that
the GPT4-Vision model demonstrates strong end-to-end embodied decision-making
abilities, outperforming GPT4-HOLMES in terms of average decision accuracy
(+3%). However, this performance is exclusive to the latest GPT4-Vision model,
surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate
that powerful MLLMs like GPT4-Vision hold promise for decision-making in
embodied agents, offering new avenues for MLLM research. Code and data are open
at https://github.com/pkunlp-icler/PCA-EVAL/.

Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition;Robotics