LLM-IK: Solving Inverse Kinematics Using Large Language Models

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Abstract
Inverse Kinematics (IK) is an integral part of robot manipulation. IK can be challenging to solve, and many computer-aided approaches have been proposed but each has its limitations. The emergence of Large Language Models (LLMs) has seen them applied to solving complex tasks including mathematical problems. This work proposes “LLM-IK” to utilize LLMs to solve IK problems. Relevant serial manipulator information is extracted from descriptor files, prompt engineered, and then provided to the LLMs with methods and feedback to interact with and learn about the kinematic chain. Multiple methods of breaking down kinematic chains into distinct sub-problems are implemented allowing for incremental problem solving. Experiments showed LLM-IK solves up to six Degrees-of-Freedom (DOF) and outperforms IKFast, TRAC-IK, and IKPy in both accuracy and solving time, highlighting this methodology can produce highly efficient and human-readable solutions.
Funding Information
  • Vector Scholarship in Artificial Intelligence
  • Vector Institute
  • Government of Canada’s New Frontiers in Research Fund (NFRFE-2024-00994)

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