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Visuospatial Skill Learning for Robots

posted Jun 5, 2017, 7:03 PM by Reza Ahmadzadeh   [ updated Jun 23, 2017, 9:07 PM ]
S. Reza Ahmadzadeh, Fulvio Mastrogiovanni, Petar Kormushev

Reference:
S. Reza Ahmadzadeh, Fulvio Mastrogiovanni, Petar Kormushev, "Visuospatial Skill Learning for Robots",
arXiv preprint arXiv:1706.00989, 2017.
Bibtex Entry:
@ARTICLE{ahmadzadeh2017visuospatial, TITLE={Visuospatial Skill Learning for Robots}, AUTHOR={Ahmadzadeh, S. Reza and Mastrogiovanni, Fulvio and Kormushev, Petar}, JOURNAL={ar{X}iv preprint ar{X}iv:1706.00989}, YEAR={2017}, PAGES={1--24}, MONTH={June}, }
Abstract:
A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial
skills for object manipulation tasks. Visuospatial skills are attained by observing spatial
relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL)
is a goal-based approach that focuses on achieving a desired goal configuration of objects relative
to one another while maintaining the sequence of operations. VSL is capable of learning and
generalizing multi-operation skills from a single demonstration, while requiring minimum prior
knowledge about the objects and the environment. In contrast to many existing approaches, VSL
offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can
be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing
techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL,
Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed
actions are learned using VSL, while the sensorimotor skills are learned using a conventional
trajectory-based learning approach. such integration easily extends robot capabilities to novel
situations, even by users without programming ability. In VSL-SP the internal planner of VSL is
integrated with an existing action-level symbolic planner. Using the underlying constraints of the
task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is
updated. Therefore the planner maintains a generalized representation of each skill as a reusable
action, which can be used in planning and performed independently during the learning phase.
The proposed approach is validated through several real-world experiments.

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