@misc {pub5984,
	title = {Human-Robot interaction with Large Language Models},
	author = {Michael Gienger},
	year = {2024},
	month = {November},
	abstract = {The recent breakthroughs in Generative AI offer fantastic opportunities to research novel concepts for intelligent embodied agents. In this keynote, I will introduce our findings in designing interactive robot agents that learn from humans, and that can exploit their acquired knowledge in different situations. We designed a Virtual Playground, in which we conducted user studies to understand the efficiency of robot curiosity as well as of multi-modal cues with respect to an explainable interaction. To close the gap towards behavior generation, I will introduce recent research in exploiting Large Language Models (LLMs) for robot task and motion planning. We combined reasoning, planning, and motion generation, and introduced a novel concept for correcting errors during planning and execution. I{\textquoteright}ll show several results both in simulations and real-world tasks for tasks like block arrangement, cocktail, and pizza preparation. I will then discuss our recent concept of {\textquotedblleft}Attentive Support{\textquotedblright}, in which we made the step from LLM-based autonomous problem-solving capabilities to human-robot group constellations and conclude with my view of interesting future research questions.},
	publisher = {Honda Research institute Europe},
	booktitle = {Keynote at the 12th Human Agent Interaction Conference, Swansea University, UK }
}