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AI Research
arXiv CS.AI·Jun 10·5 min read
Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
TL;DR
Multimodal Large Language Models (MLLMs) struggle with physical tool use, which is crucial for embodied AI applications. To investigate this, PhysTool-Bench was developed as a benchmark to assess MLLMs' understanding and planning of tool use in real-world scenarios.
✦ Why It Matters
Engineers can leverage these insights to improve MLLMs' tool recognition and planning capabilities for real-world applications.
Key Takeaways
- Explore PhysTool-Bench for evaluating MLLMs in tool use.
- Focus on improving tool recognition in realistic scenes.
- Enhance planning algorithms to better map tools to tasks.
- Investigate commonsense reasoning to bridge performance gaps.
Full Summary
Multimodal Large Language Models (MLLMs) are increasingly used in embodied AI, where they guide robots in interacting with the physical world. However, their ability to effectively use physical tools has not been thoroughly examined.
To address this, PhysTool-Bench was created, a benchmark consisting of 2,510 queries related to 2,678 real-world tools across various fields like manufacturing and healthcare. The evaluation focused on two main aspects: recognizing tools in a scene and planning their use based on instructions and visual context.
Results revealed that even the top-performing model, Gemini-3.1-Pro, identified only 58.7% of tools and completed just 21.0% of tasks end-to-end. This indicates a significant gap in MLLMs' perception and planning capabilities, suggesting a lack of functional commonsense in mapping tools to tasks.
These findings point to critical challenges for advancing practical embodied AI applications.