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Interactive Robot Learning of Visuospatial Skills

posted May 30, 2016, 2:44 PM by Reza Ahmadzadeh   [ updated Jun 26, 2017, 8:02 AM ]
Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell

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} }
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.

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