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Action learning and grounding in simulated human–robot interactions

Part of: Adaptive Learning Agents 2018

Published online by Cambridge University Press:  12 November 2019

Oliver Roesler Open the ORCID record for Oliver Roesler [Opens in a new window]  and
Ann Nowé
Oliver Roesler*
Affiliation:
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium; e-mails: [email protected], [email protected]
Ann Nowé
Affiliation:
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium; e-mails: [email protected], [email protected]
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Abstract

In order to enable robots to interact with humans in a natural way, they need to be able to autonomously learn new tasks. The most natural way for humans to tell another agent, which can be a human or robot, to perform a task is via natural language. Thus, natural human–robot interactions also require robots to understand natural language, i.e. extract the meaning of words and phrases. To do this, words and phrases need to be linked to their corresponding percepts through grounding. Afterward, agents can learn the optimal micro-action patterns to reach the goal states of the desired tasks. Most previous studies investigated only learning of actions or grounding of words, but not both. Additionally, they often used only a small set of tasks as well as very short and unnaturally simplified utterances. In this paper, we introduce a framework that uses reinforcement learning to learn actions for several tasks and cross-situational learning to ground actions, object shapes and colors, and prepositions. The proposed framework is evaluated through a simulated interaction experiment between a human tutor and a robot. The results show that the employed framework can be used for both action learning and grounding.

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e13
Copyright
© Cambridge University Press, 2019 

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