LaMI: Large Language Models for Multi-Modal Human-Robot Interaction

Abstract

This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "atomic actions" and expressions the robot can use, and offering a set of examples. Implemented on a physical robot, it demonstrates proficiency in adapting to multi-modal inputs and determining the appropriate manner of action to assist humans with its arms, following researchers' defined guidelines. Simultaneously, it coordinates the robot's lid, neck, and ear movements with speech output to produce dynamic, multi-modal expressions. This showcases the system's potential to revolutionize HRI by shifting from conventional, manual state-and-flow design methods to an intuitive, guidance-based, and example-driven approach. Supplementary material can be found at https://hri-eu.github.io/Lami/

10 pages, 6 figures

References in corpus (4)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• ChatGPT for Robotics: Design Principles and Model Abilities
• Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
• Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

Cited by in corpus (7)

• The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
• When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration
• To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
• A Framework for Adapting Human-Robot Interaction to Diverse User Groups
• Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
• Mirror Eyes: Explainable Human-Robot Interaction at a Glance