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Citation

BibTex format

@inbook{Ahmadzadeh:2016,
author = {Ahmadzadeh, SR and Mastrogiovanni, F and Kormushev, P},
title = {Visuospatial Skill Learning for Robots},
url = {http://arxiv.org/abs/1706.00989v1},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - CHAP
AB - A novel skill learning approach is proposed that allows a robot to acquirehuman-like visuospatial skills for object manipulation tasks. Visuospatialskills are attained by observing spatial relationships among objects throughdemonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-basedapproach that focuses on achieving a desired goal configuration of objectsrelative to one another while maintaining the sequence of operations. VSL iscapable of learning and generalizing multi-operation skills from a singledemonstration, while requiring minimum prior knowledge about the objects andthe environment. In contrast to many existing approaches, VSL offerssimplicity, efficiency and user-friendly human-robot interaction. We also showthat VSL can be easily extended towards 3D object manipulation tasks, simply byemploying point cloud processing techniques. In addition, a robot learningframework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and aconventional planning method. In VSL-SP, the sequence of performed actions arelearned using VSL, while the sensorimotor skills are learned using aconventional trajectory-based learning approach. such integration easilyextends robot capabilities to novel situations, even by users withoutprogramming ability. In VSL-SP the internal planner of VSL is integrated withan existing action-level symbolic planner. Using the underlying constraints ofthe task and extracted symbolic predicates, identified by VSL, symbolicrepresentation of the task is updated. Therefore the planner maintains ageneralized representation of each skill as a reusable action, which can beused in planning and performed independently during the learning phase. Theproposed approach is validated through several real-world experiments.
AU - Ahmadzadeh,SR
AU - Mastrogiovanni,F
AU - Kormushev,P
PY - 2016///
TI - Visuospatial Skill Learning for Robots
UR - http://arxiv.org/abs/1706.00989v1
ER -