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  4. Language-conditioned reinforcement learning to solve misunderstandings with action corrections
 
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Language-conditioned reinforcement learning to solve misunderstandings with action corrections

Publikationstyp
Conference Paper not in Proceedings
Date Issued
2022-11-17
Sprache
English
Author(s)
Röder, Frank  
Eppe, Manfred  
Institut
Data Science Foundations E-21  
TORE-URI
http://hdl.handle.net/11420/14361
Citation
36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Contribution to Conference
36th Conference on Neural Information Processing Systems, NeurIPS 2022  
Publisher DOI
10.48550/arXiv.2211.10168
ArXiv ID
2211.10168
Peer Reviewed
true
Cites
https://openreview.net/forum?id=lWd0qiv9E-
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.
Subjects
reinforcement learning
instruction-following
action correction
misunderstanding
ambiguity
negation
DDC Class
004: Informatik
Funding(s)
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  
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
TUHH
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