One-shot learning of human–robot handovers with triadic interaction meshes

Year
2018
Type(s)
Author(s)
David Vogt, Simon Stepputtis, Bernhard Jung, Heni Ben Amor
Source
Autonomous Robots, 42(5): 1053-1065, 2018
Url
https://doi.org/10.1007/s10514-018-9699-4
BibTeX
BibTeX

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.