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- W4309587412 abstract "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." @default.
- W4309587412 created "2022-11-28" @default.
- W4309587412 creator A5050108731 @default.
- W4309587412 creator A5056765363 @default.
- W4309587412 date "2022-11-18" @default.
- W4309587412 modified "2023-09-27" @default.
- W4309587412 title "Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections" @default.
- W4309587412 doi "https://doi.org/10.48550/arxiv.2211.10168" @default.
- W4309587412 hasPublicationYear "2022" @default.
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