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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
Publikationsdatum
2022-11-17
Sprache
English
Author
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 
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.
Schlagworte
reinforcement learning
instruction-following
action correction
misunderstanding
ambiguity
negation
DDC Class
004: Informatik
Projekt(e)
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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