Röder, FrankFrankRöderEppe, ManfredManfredEppe2022-12-142022-12-142022-11-1736th Conference on Neural Information Processing Systems (NeurIPS 2022)http://hdl.handle.net/11420/14361Human-to-human conversation is not just talking and listening. It is an incremental process where participants continually establish a common understanding to rule out misunderstandings. Current language understanding methods for intelligent robots do not consider this. There exist numerous approaches considering non-understandings, but they ignore the incremental process of resolving misunderstandings. In this article, we present a first formalization and experimental validation of incremental action-repair for robotic instruction-following based on reinforcement learning. To evaluate our approach, we propose a collection of benchmark environments for action correction in language-conditioned reinforcement learning, utilizing a synthetic instructor to generate language goals and their corresponding corrections. We show that a reinforcement learning agent can successfully learn to understand incremental corrections of misunderstood instructions.enreinforcement learninginstruction-followingaction correctionmisunderstandingambiguitynegationInformatikConference Paper not in Proceedings10.48550/arXiv.2211.101682211.10168Conference Paper