Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

TY - JOUR

T1 - Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins

AU - Ranasinghe, Anuradha

AU - Sornkarn, Nantachai

AU - Dasgupta, Prokar

AU - Althoefer, Kaspar

AU - Penders, Jacques

AU - Nanayakkara, Thrishantha

PY - 2016/2

Y1 - 2016/2

N2 - This paper presents salient features of human-human interaction where 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 salient features of the interaction between the guider and the follower such as a) the order of an autoregressive control policy that maps states of the follower to actions of the guider, b) how the guider may modulate the pulling force in response to the confidence level of the follower, and c) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a $3^{\rm rd}$ order auto-regressive predictive control policy and followers tend to adopt $2^{\rm nd}$ order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the confidence level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the confidence level of the follower monitored using a time varying virtual damped inertial model.

AB - This paper presents salient features of human-human interaction where 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 salient features of the interaction between the guider and the follower such as a) the order of an autoregressive control policy that maps states of the follower to actions of the guider, b) how the guider may modulate the pulling force in response to the confidence level of the follower, and c) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a $3^{\rm rd}$ order auto-regressive predictive control policy and followers tend to adopt $2^{\rm nd}$ order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the confidence level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the confidence level of the follower monitored using a time varying virtual damped inertial model.

KW - Human robot interaction

KW - Haptic based guidance

KW - Robotics

KW - Predictive control

U2 - 10.1109/TCYB.2015.2409772

DO - 10.1109/TCYB.2015.2409772

M3 - Article

SN - 1083-4419

VL - 46

SP - 568

EP - 579

JO - IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics

IS - 2

ER -