Human 0, MLLM 1: Unlocking New Layers of Automation in Language-Conditioned Robotics with Multimodal LLMs

Language-conditioned robotics has seen tremendous growth in frameworks that aim to improve the success rates of robots acting upon the environment according to free-form language instructions. However, most existing frameworks leverage a human in the loop to assist with critical functions. Humans are mainly involved in ensuring that a human-requested task is feasible, resetting the robot when it diverges from achieving the requested goal, and deciding if it has completed the task. As human involvement limits the scalability of language-conditioned robotics, we propose automating these human functions through Multimodal Large Language Models in the Loop (MLLM-IL). We conduct experiments leveraging multimodal large language models, specifically OpenAI's GPT-4, and Google Gemini, to evaluate their potential in automating crucial functions. The introduced new layers of automation include analyzing task feasibility, assessing task progress, and detecting task success. We investigate how different factors, including the choice of LLM, image resolution of the input images, and the structure of the prompt, affect the performance of the LLMs in achieving the target functions. Results show significant zero-shot success with feasibility analysis accuracies exceeding 90%. Our work demonstrates the immense potential of utilizing MLLM-IL to complement existing frameworks in language-conditioned robotics, opening the space for a wealth of new applications.