Identification of Haptic Based Guiding Using Hard Reins
Identifieur interne : 003B10 ( Ncbi/Merge ); précédent : 003B09; suivant : 003B11Identification of Haptic Based Guiding Using Hard Reins
Auteurs : Anuradha Ranasinghe [Royaume-Uni] ; Prokar Dasgupta [Royaume-Uni] ; Kaspar Althoefer [Royaume-Uni] ; Thrishantha Nanayakkara [Royaume-Uni]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- methods : Robotics.
- physiology : Auditory Perception, Visual Perception.
- Algorithms, Humans, Interpersonal Relations, Models, Theoretical.
Abstract
This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower’s dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment.
Url:
DOI: 10.1371/journal.pone.0132020
PubMed: 26201076
PubMed Central: 4511788
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<front><div type="abstract" xml:lang="en"><p>This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3<sup>rd</sup>
and 2<sup>nd</sup>
order auto-regressive predictive and reactive control policies respectively. By modeling the follower’s dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment.</p>
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<front><div type="abstract" xml:lang="en"><p>This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3<sup>rd</sup>
and 2<sup>nd</sup>
order auto-regressive predictive and reactive control policies respectively. By modeling the follower’s dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment.</p>
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<author><name sortKey="Nanayakkara, Thrishantha" sort="Nanayakkara, Thrishantha" uniqKey="Nanayakkara T" first="Thrishantha" last="Nanayakkara">Thrishantha Nanayakkara</name>
<affiliation wicri:level="3"><nlm:affiliation>Department of Informatics/ Center for Robotics Research, King's College London, London, United Kingdom.</nlm:affiliation>
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<author><name sortKey="Nanayakkara, Thrishantha" sort="Nanayakkara, Thrishantha" uniqKey="Nanayakkara T" first="Thrishantha" last="Nanayakkara">Thrishantha Nanayakkara</name>
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<front><div type="abstract" xml:lang="en">This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment.</div>
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