Abstract
In this paper, we present a novel shared-control telemanipulation method that is designed to incrementally improve a user»s motor ability. Our method initially corrects for the user»s suboptimal control trajectories, gradually giving the user more direct control over a series of training trials as he/she naturally gets more accustomed to the task. Our shared-control method, calledShared Dynamic Curves, blends suboptimal user translation and rotation control inputs with known translation and rotation paths needed to complete a task. Shared Dynamic Curves provide a translation and rotation path in space along which the user can easily guide the robot, and this curve can bend and flex in real-time as a dynamical system to pull the user»s motion gracefully toward a goal. We show through a user study that Shared Dynamic Curves affords effective motor learning on certain tasks compared to alternative training methods. We discuss our findings in the context of shared control and speculate on how this method could be applied in real-world scenarios such as job training or stroke rehabilitation.
DOI: 10.1145/3171221.3171278
BibTex
@inproceedings{Rakita_2018,
doi = {10.1145/3171221.3171278},
url = {https://doi.org/10.1145%2F3171221.3171278},
year = 2018,
month = {feb},
publisher = {{ACM}},
author = {Daniel Rakita and Bilge Mutlu and Michael Gleicher and Laura M. Hiatt},
title = {Shared Dynamic Curves},
booktitle = {Proceedings of the 2018 {ACM}/{IEEE} International Conference on Human-Robot Interaction}
}