Interactive Robot Learning of Visuospatial Skills

Post date: May 30, 2016 9:44:0 PM

Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell

Reference:

Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell, "Interactive Robot Learning of

Visuospatial Skills", In Proc. 16th IEEE Intl Conf. on Advanced Robotics (ICAR 2013), Montevideo,

Uruguay, 25-29 Nov. 2013.

Bibtex Entry:

@INPROCEEDINGS{ahmadzadeh2013interactive, TITLE={Interactive Robot Learning of Visuospatial Skills}, AUTHOR={Ahmadzadeh, Seyed Reza and Kormushev, Petar and Caldwell, Darwin. G.}, BOOKTITLE={Advanced Robotics ({ICAR}), 16th International Conference on}, PAGES={1--8}, YEAR={2013}, ORGANIZATION={IEEE}, DOI={10.1109/ICAR.2013.6766597} }

DOI:

10.1109/ICAR.2013.6766597

Abstract:

This paper proposes a novel interactive robot learning approach for acquiring visuospatial skills.

It allows a robot to acquire new capabilities by observing a demonstration while interacting with a

human caregiver. Most existing learning from demonstration approaches focus on the trajectories,

whereas in our approach the focus is placed on achieving a desired goal configuration of objects

relative to one another. Our approach is based on visual perception which captures the object's

context for each demonstrated action. The context embodies implicitly the visuospatial

representation including the relative positioning of the object with respect to multiple other

objects simultaneously. The proposed approach is capable of learning and generalizing different

skills such as object reconfiguration, classification, and turn-taking interaction. The robot

learns to achieve the goal from a single demonstration while requiring minimum a priori knowledge

about the environment. We illustrate the capabilities of our approach using four real world

experiments with a Barrett WAM robot.