PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding (2501.16411v2)

Abstract: Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. While Vision-LLMs (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend physical phenomena remains extremely limited. To close this gap, we introduce PhysBench, a comprehensive benchmark designed to evaluate VLMs' physical world understanding capability across a diverse set of tasks. PhysBench contains 10,002 entries of interleaved video-image-text data, categorized into four major domains: physical object properties, physical object relationships, physical scene understanding, and physics-based dynamics, further divided into 19 subclasses and 8 distinct capability dimensions. Our extensive experiments, conducted on 75 representative VLMs, reveal that while these models excel in common-sense reasoning, they struggle with understanding the physical world -- likely due to the absence of physical knowledge in their training data and the lack of embedded physical priors. To tackle the shortfall, we introduce PhysAgent, a novel framework that combines the generalization strengths of VLMs with the specialized expertise of vision models, significantly enhancing VLMs' physical understanding across a variety of tasks, including an 18.4\% improvement on GPT-4o. Furthermore, our results demonstrate that enhancing VLMs' physical world understanding capabilities can help embodied agents such as MOKA. We believe that PhysBench and PhysAgent offer valuable insights and contribute to bridging the gap between VLMs and physical world understanding.

PhysBench: Benchmarking Vision-LLMs for Physical World Understanding

The paper introduces PhysBench, a comprehensive benchmark toolkit designed to evaluate and enhance the capabilities of Vision-LLMs (VLMs) in understanding the physical world. VLMs excel in reasoning and task planning, but face notable limitations when interpreting physical phenomena. PhysBench seeks to address this gap through a dataset encompassing 100,000 events with video, image, and text modalities across four major domains: physical object properties, object relationships, scene understanding, and dynamics, subdivided into 19 subclasses and eight distinct capability dimensions.

Key Findings and Contributions

1. Evaluation on Existing VLMs: The authors conducted extensive experiments with 75 VLMs, revealing that while these models perform well in reasoning tasks, they are deficient in understanding physical dynamics and scenes. This is attributed to the lack of such data in their training sets. Closed-source models generally perform better than their open-source counterparts, suggesting a significant gap in performance due to data quality and availability.
2. Introduction of PhysAgent: To address the deficiencies, the paper proposes PhysAgent, a framework that integrates generalization strengths of VLMs with the specialized insight of vision experts to enhance physical understanding. PhysAgent leverages foundation models and a physics knowledge memory to improve physical event interpretation, achieving an 18.4% performance improvement on tasks using the GPT-4o model.
3. Implications for Embodied AI: The enhanced understanding capabilities facilitated by PhysBench and PhysAgent can significantly support the deployment of embodied agents in real-world scenarios, as evidenced by experimental validations with robotic agents such as MOKA. These benchmarks could drive improvements in safety, functionality, and task complexity that VLM-based agents can perform.

Implications for Future AI Development

PhysBench and PhysAgent are not just benchmarks but tools with the potential to streamline the development of AI systems capable of comprehensive physical world understanding. This has far-reaching implications for the field, offering a structured path for developing AI systems that integrate deeper physical insights. As VLMs increasingly handle multimodal inputs, leveraging datasets like PhysBench can accelerate progress in AI fields such as robotics, autonomous systems, and interactive applications where an understanding of physical laws and dynamics is crucial.

Future Directions

The research opens several avenues for future exploration. Integrating diverse sources of physical world data could further enhance PhysAgent's robustness. Additionally, collaboration across domains to integrate insights from physics, computer vision, and AI could lead to more sophisticated VLMs capable of unprecedented levels of interaction and understanding. Furthermore, extending benchmarks to include more complex physical scenarios and interactions can drive continuous improvement in AI capabilities, fostering systems that closely mirror human-like understanding of the physical world.

In summary, PhysBench represents a meaningful contribution to the AI field, setting a new standard for how VLMs should be evaluated and developed concerning physical world interactions. Its introduction marks a significant step towards closing the gap in system limitations and aiding the development of more intuitive and intelligent embodied AI agents.