arXiv ID: 2211.10168
Title: Language-conditioned reinforcement learning to solve misunderstandings with action corrections
Language: English
Authors: Röder, Frank 
Eppe, Manfred 
Keywords: reinforcement learning; instruction-following; action correction; misunderstanding; ambiguity; negation
Issue Date: 17-Nov-2022
Source: 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Abstract (english): 
Human-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.
Conference: 36th Conference on Neural Information Processing Systems, NeurIPS 2022 
Institute: Data Science Foundations E-21 
Document Type: Conference Paper (not in Proceedings)
Project: Lernen von konversationaller Aktionsreparatur für intelligente Roboter 
SPP 2134: Modellierung des peripersonalen Raumes und Körperschemas eines Roboters für adaptives Lernen und Imitation 
SPP 2134: Ideomotor Transfer for Active Self-Emergence 
Funded by: Deutsche Forschungsgemeinschaft (DFG) 
Peer Reviewed: Yes
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