Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling
This paper introduces the notion of danger awareness in the context of Human-Robot Interaction (HRI), which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in enforcing the safety. This paper also proposes a method to quantify th...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
09.02.2021
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Subjects | |
Online Access | Get full text |
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Summary: | This paper introduces the notion of danger awareness in the context of
Human-Robot Interaction (HRI), which decodes whether a human is aware of the
existence of the robot, and illuminates whether the human is willing to engage
in enforcing the safety. This paper also proposes a method to quantify this
notion as a single binary variable, so-called danger awareness coefficient. By
analyzing the effect of this coefficient on the human's actions, an online
Bayesian learning method is proposed to update the belief about the value of
the coefficient. It is shown that based upon the danger awareness coefficient
and the proposed learning method, the robot can build a predictive human model
to anticipate the human's future actions. In order to create a communication
channel between the human and the robot, to enrich the observations and get
informative data about the human, and to improve the efficiency of the robot,
the robot is equipped with a danger signaling system. A predictive planning
scheme, coupled with the predictive human model, is also proposed to provide an
efficient and Probabilistically safe plan for the robot. The effectiveness of
the proposed scheme is demonstrated through simulation studies on an
interaction between a self-driving car and a pedestrian. |
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DOI: | 10.48550/arxiv.2102.05144 |