Visuospatial Skill Learning for Object Reconfiguration Tasks

Post date: May 29, 2016 5:37:51 PM

Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell

Reference:

Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell, "Visuospatial Skill Learning for Object

Reconfiguration Tasks", In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS 2013),

Tokyo, Japan, pp. 685-691, 3-8 Nov. 2013.

Bibtex Entry:

@INPROCEEDINGS{ahmadzadeh2013visuospatial, TITLE={Visuospatial Skill Learning for Object Reconfiguration Tasks}, AUTHOR={Ahmadzadeh, Seyed Reza and Kormushev, Petar and Caldwell, Darwin G.}, BOOKTITLE={Intelligent Robots and Systems ({IROS}), {IEEE/RSJ} International Conference on}, PAGES={685--691}, YEAR={2013}, Month={November}, ADDRESS={Tokyo, Japan}, ORGANIZATION={IEEE}, DOI={10.1109/IROS.2013.6696425} }

DOI:

10.1109/IROS.2013.6696425

Abstract:

We present a novel robot learning approach based on visual perception that allows a robot to

acquire new skills by observing a demonstration from a tutor. Unlike most existing learning from

demonstration approaches, where the focus is placed on the trajectories, in our approach the focus

is 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. This

context is the basis of the visuospatial representation and encodes implicitly the relative

positioning of the object with respect to multiple other objects simultaneously. The proposed

approach is capable of learning and generalizing multi-operation skills from a single demonstration,

while requiring minimum a priori knowledge about the environment. The learned skills comprise a

sequence of operations that aim to achieve the desired goal configuration using the given objects.

We illustrate the capabilities of our approach using three object reconfiguration tasks with a

Barrett WAM robot.