We propose an imitation learning methodology that allows robots to seamlessly retrieve and pass objects to and from human users. Instead of hand-coding interaction parameters, we extract relevant information such as joint correlations and spatial relationships from a single task demonstration of two humans. At the center of our approach is an interaction model that enables a robot to generalize an observed demonstration spatially and temporally to new situations. To this end, we propose a data-driven method for generating interaction meshes that link both interaction partners to the manipulated object. The feasibility of the approach is evaluated in a within user study which shows that human–human task demonstration can lead to more natural and intuitive interactions with the robot.
@article{Vogt2018,
doi = {10.1007/s10514-018-9699-4},
url = {https://doi.org/10.1007/s10514-018-9699-4},
year = {2018},
month = feb,
publisher = {Springer Science and Business Media {LLC}},
volume = {42},
number = {5},
pages = {1053--1065},
author = {David Vogt and Simon Stepputtis and Bernhard Jung and Heni Ben Amor},
title = {One-shot learning of human{\textendash}robot handovers with triadic interaction meshes},
journal = {Autonomous Robots}
}