SLHAP: Simultaneous Learning of Hierarchy and Primitives
Abstract
In robot learning from demonstration (LfD), a human teaches a robot how to perform a task by executing the task himself. For complex tasks, such as the tire rotation shown in the accompanying video, this involves learning at two levels: the robot needs to learn the motion primitives and also how these primitives are combined into a hierarchy of steps to achieve the complete task. These two kinds of LfD have traditionally been studied separately. The contribution of this work is a novel humanrobot interaction paradigm, called SLHAP (for simultaneous learning of hierarchy and primitives), in which these two kinds of LfD are interleaved in a way that is natural for a human teacher. We have implemented a SLHAP proof of concept system in which an autonomous robot learns from a human teacher through a mixture of narration, in which the human speaks the name of a primitive when he executes it, and dialogue, in which the human answers the robot’s questions about how to group primitives into subtasks. The human’s motions are also tracked using a Vicon motion capture system.
BibTeX
@conference{Mohseni-Kabir-2017-126718,author = {Anahita Mohseni-Kabir and Changshuo Li and Victoria Wu and Daniel Miller and Benjamin Hylak and Sonia Chernova and Dmitry Berenson and Candace Sidner and Charles Rich},
title = {SLHAP: Simultaneous Learning of Hierarchy and Primitives},
booktitle = {Proceedings of Companion of the ACM/IEEE International Conference on Human-Robot Interaction (HRI '17)},
year = {2017},
month = {March},
pages = {412},
}