Non-prehensile tool-object manipulation by integrating LLM-based planning and manoeuvrability-driven controls
Lee, Hoi-Yin Zhou, Peng Duan, Anqing Ma, Wanyu Yang, Chenguang Navarro-Alarcon, David
Lee, Hoi-Yin
Zhou, Peng
Duan, Anqing
Ma, Wanyu
Yang, Chenguang
Navarro-Alarcon, David
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Robotics
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Journal article
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English
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Abstract
The ability to wield tools was once considered exclusive to human intelligence, but it is now known that many other animals, like crows, possess this capability. Yet, robotic systems still fall short of matching biological dexterity. In this paper, we investigate the use of Large Language Models (LLMs), tool affordances, and object manoeuvrability for non-prehensile tool-based manipulation tasks. Our novel method leverages LLMs based on scene information and natural language instructions to enable symbolic task planning for tool-object manipulation. This approach allows the system to convert a human language sentence into a sequence of feasible motion functions. We have developed a novel manoeuvrability-driven controller using a new tool affordance model derived from visual feedback. This controller helps guide the robot’s tool utilization and manipulation actions, even within confined areas, using a stepping incremental approach. The proposed methodology is evaluated with experiments to prove its effectiveness under various manipulation scenarios.
Citation
Lee, H.-Y., Zhou, P., Duan, A., Ma, W., Yang, C., Navarro-Alarcon, D. (2026). Non-prehensile tool-object manipulation by integrating LLM-based planning and manoeuvrability-driven controls. Robotics and Computer-Integrated Manufacturing, 100, 103231-103231. https://doi.org/10.1016/j.rcim.2026.103231
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Robotics and Computer-Integrated Manufacturing
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Information and Computing Sciences, Artificial Intelligence
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Elsevier