Imperial College London

Dr Thrishantha Nanayakkara

Faculty of EngineeringDyson School of Design Engineering

Reader in Design Engineering and Robotics
 
 
 
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Contact

 

+44 (0)20 7594 0965t.nanayakkara Website CV

 
 
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Location

 

RCS1 M229Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ranasinghe:2018:10.1109/LRA.2018.2821273,
author = {Ranasinghe, A and Dasgupta, P and Nagar, A and Nanayakkara, D},
doi = {10.1109/LRA.2018.2821273},
journal = {IEEE Robotics and Automation Letters},
pages = {2624--2631},
title = {Human behavioral metrics of a predictive model emerging during robot assisted following without visual feedback},
url = {http://dx.doi.org/10.1109/LRA.2018.2821273},
volume = {3},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Robot-assisted guiding is gaining increased interest due to many applications involving moving in the noisy and low visibility environments. In such cases, haptic feedback is the most effective medium to communicate. In this letter, we focus on perturbation-based haptic feedback due to applications like guide dogs for visually impaired people and potential robotic counterparts providing haptic feedback via reins to assist indoor fire fighting. Since proprioceptive sensors like spindles and tendons are part of the muscles involved in the perturbation, haptic perception becomes a coupled phenomenon with spontaneous reflex muscle activity. The nature of this interplay and how the model-based sensory-motor integration evolves during haptic-based guiding is not well understood yet. We asked human followers to hold the handle of a hard rein attached to a one-DoF robotic arm that gave perturbations to the hand to correct an angle error of the follower. We found that followers start with a second-order reactive autoregressive following model and changes it to a predictive model with training. The reduction in cocontraction of muscles and leftward/rightward asymmetry of a set of followers behavioral metrics show that the model-based prediction accounts for the internal coupling between proprioception and muscle activity during perturbation responses.
AU - Ranasinghe,A
AU - Dasgupta,P
AU - Nagar,A
AU - Nanayakkara,D
DO - 10.1109/LRA.2018.2821273
EP - 2631
PY - 2018///
SN - 2377-3766
SP - 2624
TI - Human behavioral metrics of a predictive model emerging during robot assisted following without visual feedback
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2018.2821273
UR - http://hdl.handle.net/10044/1/58650
VL - 3
ER -