LLM-controller: Dynamic robot control adaptation using large language models

Abstract

In this study, a dynamic adaptation of a robot controller is investigated using large language models (LLMs). We propose our controller called the LLM-Controller, where, in response to changes in the system dynamics or reference signals, the LLM adapts the controller to the new context. Various scenarios reflecting real-world conditions, including unknown disturbances, unmodeled dynamics, and changing reference signals, were analyzed. Using the proposed LLM-Controller, one can adapt to new conditions automatically without manual tuning. Additionally, the controller's performance is investigated using different prompting techniques, such as zero-shot and few-shot chain-of-thought (COT), which facilitate step-by-step reasoning and improve adaptation to new contexts. The proposed scheme is applied to two case studies involving robot manipulators. First, it is tested on a 2-link robot manipulator, followed by a 3-link manipulator to enhance its generalizability. The algorithm's adaptability and effectiveness are further evaluated across a range of tasks and conditions, demonstrating its versatility in various scenarios. The results demonstrate that the LLM-Controller achieved a 100 % success rate in adapting the controller to new conditions for the 2-link manipulator, with a significant improvement in trial efficiency; while for the 3-link system, the controller maintained a 90 % success rate, showing greater adaptability to changes in reference signals or dynamic conditions in under 20 s. These outcomes could be further enhanced by employing a COT approach, potentially leading to higher success rates, fewer trials, and optimized costs. In contrast, the classic nonlinear adaptive controller struggled to adjust to the new conditions, while the LLM-Controller automatically adapts, guiding the system to new stable states. This research provides valuable insights into how LLMs can enhance decision-making, improving stability and performance in dynamic and uncertain environments.