Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
# MLLMs Face Gaps in Physical Tool Use for Robotics
Researchers examined how Multimodal Large Language Models—AI systems that process both text and images—perform when instructing robots to use actual physical tools rather than digital software. The study, focusing on embodied AI applications, revealed significant limitations in these models' ability to guide robots through real-world tool interactions. While MLLMs have demonstrated competency directing robots via APIs and digital interfaces, their performance deteriorates when tasks require understanding of physical constraints, tool mechanics, and real-world manipulation.
For robotics integrators and automation coordinators, this finding highlights a critical gap between current MLLM capabilities and operational requirements. Robot systems relying on these models as decision-making "brains" may struggle with tasks involving hammers, wrenches, cutting tools, or other equipment requiring spatial reasoning and force feedback. This constraint affects mission planning, particularly in logistics, field robotics, and autonomous systems where physical tool use is routine. Teams currently designing human-robot coordination workflows should account for this limitation when assigning autonomy levels.
The research underscores a practical distinction: visual understanding alone does not equate to competent physical task execution. Organizations deploying embodied AI should consider hybrid approaches—combining MLLM decision-making with specialized controllers or human oversight for tool-intensive operations—rather than assuming these models can autonomously manage complex physical interactions without additional safeguards or system architecture adjustments.