arXiv:2606.10803v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world. In such embodied settings, a central capability is the use of physical tools, which underpins MLLMs' ability to assist humans in real-world tasks. Despite the importance, MLLMs' proficiency in physical tool use remains largely unexplored. To address this gap, we introduce PhysTool-Bench, the first physical tool-use benchmark designed to evaluate MLLMs' ability to com
The rapid advancement of MLLMs and their increasing application in embodied AI necessitates a robust evaluation of their physical interaction capabilities beyond digital APIs.
This development addresses a critical gap in understanding MLLMs' real-world utility and safety in physical environments, impacting their integration into industries and daily life.
The introduction of PhysTool-Bench provides a standardized metric for evaluating physical tool use, accelerating the development and deployment of capable embodied AI.
- · AI research institutions
- · Robotics companies
- · Embodied AI developers
- · Hardware manufacturers
- · Companies relying solely on API-driven MLLM solutions
- · Theoretical AI development without real-world validation
Improved MLLMs for physical tasks will lead to more capable robotic systems.
Enhanced robotic capabilities could accelerate automation in manufacturing, logistics, and service industries.
Widespread adoption of physically adept MLLM-powered robots could redefine labor markets and human-robot collaboration paradigms.
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