Large Language Model Interface for Manipulator Control

A language model is now a prominent research topic in Artificial Intelligence (AI) that has been trained to comprehend humans’ mode of communication and converse back in same way. Large-language model (LLM) is an improved version with greater learning capacity to also absorb sophisticated language structure capabilities. Robots are being utilized in every domain for automating processes. A pivotal challenge is the necessity of technical guide to communicate with the robot. This study’s main objective is to integrate LLMs into manipulator control systems, i.e., to facilitate the input of human-language instructions, which are then seamlessly translated into precise robotic arm tasks. The study addresses challenges like interpreting vague inputs, inferring reference frames, and ensuring usability through simple queries without requiring technical expertise. The proposed method is implemented using LLM models and the Robot Operating System (ROS) and tested using multiple manipulators both in the Gazebo simulator and on real-time hardware. The model was tested with a series of prompts, and it achieved a success rate of 87.33%, highlighting the LLM’s effective understanding of human commands and the related performance of the robotic system.