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About

Joerg Deigmoeller is at the forefront of integrating Large Language Models (LLMs) into multi-modal human-robot interaction (HRI), fundamentally reshaping how robots perceive, reason, and act alongside humans. His key research areas include LLM-based robotic planning, attentive support in group interactions, and grounded reasoning for everyday environments. Deigmoeller’s major contributions are exemplified by his highly cited work, “LaMI: Large Language Models for Multi-Modal Human-Robot Interaction” (64 citations), which introduces a novel LLM-driven system that replaces complex, resource-intensive HRI designs with streamlined intent estimation and behavior generation. His follow-up paper, “CoPAL: Corrective Planning of Robot Actions with Large Language Models” (25 citations), advances autonomous task execution by enabling robots to dynamically correct plans in open-world settings. In “To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions” (14 citations), he pioneers Attentive Support, a concept that allows robots to provide unobtrusive, context-aware assistance by fusing scene perception, dialogue, and common-sense reasoning. Deigmoeller also explores multi-agent system interaction design and user interfaces for visualizing robot reasoning, ensuring transparency in human-robot collaboration. His work, accumulating over 120 citations, is shaping the next generation of socially intelligent, autonomous robots.