Facilitating intention prediction for humans by optimizing robot motions
Freek Stulp, Jonathan Grizou, Baptiste Busch, Manuel Lopes
Abstract
Members of a team are able to coordinate their actions by anticipating the intentions of others. Achieving such implicit coordination between humans and robots requires humans to be able to quickly and robustly predict the robot's intentions, i.e. the robot should demonstrate a behavior that is legible. Whereas previous work has sought to explicitly optimize the legibility of behavior, we investigate legibility as a property that arises automatically from general requirements on the efficiency and robustness of joint human-robot task completion. We do so by optimizing fast and successful completion of joint human-robot tasks through policy improvement with stochastic optimization. Two experiments with human subjects show that robots are able to adapt their behavior so that humans become better at predicting the robot's intentions early on, which leads to faster and more robust overall task completion.
BibTeX
@inproceedings{iros2015_facilitatinginte,
title = {Facilitating intention prediction for humans by optimizing robot motions},
author = {Freek Stulp and Jonathan Grizou and Baptiste Busch and Manuel Lopes},
booktitle = {IROS 2015},
year = {2015}
}