See, Symbolize, Act: Grounding VLMs with Spatial Representations for Better Gameplay
Summary: arXiv:2603.11601v2 Announce Type: replace
Abstract: Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can improve their performance in interactive environments.
Introduction
In the rapidly evolving field of artificial intelligence, Vision-Language Models (VLMs) have emerged as powerful tools capable of understanding and describing complex visual scenes. However, a significant challenge remains: the ability to translate this understanding into grounded actions within interactive environments. This article explores the potential of enhancing VLM performance by incorporating both visual frames and symbolic representations of scenes.
Methodology
To evaluate the effectiveness of integrating symbolic information, we conducted experiments using three state-of-the-art VLMs across multiple interactive platforms, including:
- Atari games
- VizDoom
- AI2-THOR
We compared various configurations:
- Frame-only pipelines
- Frame with self-extracted symbols
- Frame with ground-truth symbols
- Symbol-only pipelines
Results
The results of our experiments indicated that all models experienced performance improvements when provided with accurate symbolic information. However, the benefits varied significantly based on the method of symbol extraction:
- When VLMs utilized ground-truth symbols, performance metrics were significantly higher.
- In contrast, reliance on self-extracted symbols led to varied outcomes, heavily influenced by the model’s inherent capabilities and the complexity of the scene being analyzed.
Discussion
Our findings underscore the importance of reliable symbol extraction in improving VLM performance within interactive environments. The accuracy of the symbolic information directly impacts decision-making and gameplay outcomes. Moreover, the results revealed that when VLMs attempted to extract symbols independently, their performance became erratic, highlighting the challenges posed by scene complexity and the model’s limitations.
Conclusion
The integration of symbolic grounding in Vision-Language Models holds significant promise for enhancing their functionality in interactive tasks. However, achieving reliable symbol extraction remains a critical bottleneck. Future research should focus on improving perception quality and exploring innovative methods for symbol extraction, ensuring that VLM-based agents can effectively navigate and interact with complex environments.
Implications for Future Development
As the field of AI continues to advance, the insights gained from this study can inform the design of more robust VLMs capable of meaningful interaction. By addressing the limitations of self-extracted symbols and enhancing the quality of symbolic grounding, researchers can pave the way for more sophisticated AI systems that can seamlessly integrate visual understanding with actionable decision-making in real-world scenarios.