Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond (2310.02071v4)

Abstract: In this study, we explore the potential of Multimodal LLMs (MLLMs) in improving embodied decision-making processes for agents. While 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/.

Towards End-to-End Embodied Decision Making via Multi-modal LLM: Explorations with GPT4-Vision and Beyond

The paper "Towards End-to-End Embodied Decision Making via Multi-modal LLM: Explorations with GPT4-Vision and Beyond" proposes a novel approach to embodied decision-making by leveraging the capabilities of Multimodal LLMs (MLLMs). The research examines how state-of-the-art MLLMs like GPT4-Vision can manage decision-making tasks in an end-to-end manner, contrasting their performance with collaborative frameworks that merge LLMs and MLLMs. The focus of this paper is on the introduction of PCA-EVAL, a benchmarking suite designed to evaluate decision-making skills from the lenses of Perception, Cognition, and Action.

Key Contributions and Findings

1. PCA-EVAL Benchmark: The paper launches a new benchmark, PCA-EVAL, which is structured to assess decision-making abilities across diverse domains such as autonomous driving, domestic assistance, and gaming. The benchmark is thorough, providing a multidimensional view of agent performance by evaluating perception, cognition, and action rather than solely relying on cumulative reward metrics.
2. HOLMES Framework: Another significant contribution is the HOLMES cooperation framework that empowers LLMs to harness multimodal inputs efficiently, integrating visual information via MLLMs and interacting with APIs to enhance overall decision-making capabilities.
3. Empirical Insights: The experimental results highlight a compelling performance by GPT4-Vision in end-to-end decision-making, outperforming traditional frameworks by a margin of 3% in decision accuracy. Additionally, GPT4-Vision excels over open-source counterparts by 26%. HOLMES, while effective, exhibited that collaborative frameworks still hold potential value but require further optimization to match the streamlined efficacy of one-shot reasoning by models like GPT4-Vision.

Implications

This research firmly positions MLLMs such as GPT4-Vision as promising tools for advancing decision-making in complex environments with high dimensionality. The comparison between end-to-end and collaborative strategies underscores the necessity for a balanced approach where multimodal inputs are directly harnessed to minimize information loss typically seen in modality conversion. Notably, GPT4-Vision's performance reveals significant potential for MLLMs in simplifying embodied decision tasks that involve intricate interactions with visual and textual data.

Future Directions

The exploration of end-to-end decision-making with MLLMs opens doors to further research in the field of artificial intelligence. Future studies could focus on enhancing open-source MLLMs to match the performance of proprietary models like GPT4-Vision, ensuring broader accessibility and application. Expanding the PCA-EVAL to include more domains and a wider variety of tasks would also provide a more comprehensive evaluation framework for embodied decision-making agents.

This paper is poised to serve as a linchpin for subsequent endeavors in designing intelligent agents with refined decision-making capabilities, paving the path for seamless integration of multimodal understanding in AI-driven environments.