LMPVC and Policy Bank: Adaptive voice control for industrial robots with code generating LLMs and reusable Pythonic policies

Abstract

Modern industry is increasingly moving away from mass manufacturing, towards more specialized and personalized products. As manufacturing tasks become more complex, full automation is not always an option, human involvement may be required. This has increased the need for advanced human robot collaboration (HRC), and with it, improved methods for interaction, such as voice control. Recent advances in natural language processing, driven by artificial intelligence (AI), have the potential to answer this demand. Large language models (LLMs) have rapidly developed very impressive general reasoning capabilities, and many methods of applying this to robotics have been proposed, including through the use of code generation. This paper presents Language Model Program Voice Control (LMPVC), an LLM-based prototype voice control architecture with integrated policy programming and teaching capabilities, built for use with Robot Operating System 2 (ROS2) compatible robots. The architecture builds on prior works using code generation for voice control by implementing an additional programming and teaching system, the Policy Bank. We find this system can compensate for the limitations of the underlying LLM, and allow LMPVC to adapt to different downstream tasks without a slow and costly training process. The architecture and additional results are released on GitHub (https://github.com/ozzyuni/LMPVC).