@techreport {pub5515,
	title = {CoPal: Planning Robot Actions using Large Language Models},
	author = {Frank Joublin AND Antonello Ceravola AND Pavel Smirnov AND Felix Ocker AND J{\"o}rg Deigm{\"o}ller AND Anna Belardinelli AND Chao Wang AND Daniel Tanneberg AND Stephan Hasler AND Michael Gienger},
	year = {2023},
	month = {October},
	abstract = {Recent advances in the field of pretrained Large Language Models (LLM) made commonsense knowledge available "out of the box" for a vast range of scenarios including content generation, customer service, and voice assistants. The release of GPT-3.5 (known as ChatGPT) opened prospectives for building highly contextualizable conversational agents, capable to hold a dialog and reflect about various situations as well as on behalf of different social roles (e.g., kitchen chef, software developer). Such an advancement is highly relevant for the robotics domain, where embodied agents have to take care about generating their cyber-physical behaviour in accordance to users{\textquoteright} requests. Robotics behaviour planning comprizes combinations of manipulation and motion planning problems. The contributions of this paper are the following: {\textbullet} Adaptive manipulation planning mechanism with replanning based a pre-simulated feedbacks of different types. {\textbullet} Experimental evaluation of efficiency of the proposed replanning mechanism in two real-world use-cases (pizza, barman) {\textbullet} Evaluation of Improvization capabilities in handling non-standard situations.},
	publisher = {Arxiv},
	booktitle = {Arxiv},
	number = {arXiv:2310.07263}
}