2020
IJCAI
IJCAI 2020
Transparent Intent for Explainable Shared Control in Assistive Robotics
Abstract
Robots supplied with the ability to infer human intent have many applications in assistive robotics. In these applications, robots rely on accurate models of human intent to administer appropriate assistance. However, the effectiveness of this assistance also heavily depends on whether the human can form accurate mental models of robot behaviour. The research problem is to therefore establish a transparent interaction, such that both the robot and human understand each otherโs underlying "intent". We situate this problem in our Explainable Shared Control paradigm and present ongoing efforts to achieve transparency in human-robot collaboration.
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Interdisciplinary Bridge
โ Artificial Intelligence and Reinforcement Learning
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Keyword Pioneer
โ explainable robotics
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Cross-Pollinator
โ Artificial Intelligence, Machine Learning, Reinforcement Learning, Robotics
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Trend Setter
โ Human-Robot Interaction