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Encoding Demonstrations and Learning New Trajectories using Canal Surfaces

posted Jul 1, 2016, 8:08 AM by Reza A   [ updated Jun 24, 2017, 9:40 AM ]
Seyed Reza Ahmadzadeh, Sonia Chernova

Reference:
Seyed Reza Ahmadzadeh, Sonia Chernova, "Encoding Demonstrations and Learning New Trajectories using
Canal Surfaces", In Proc. 25th Int. joint Conf. on Artificial Intelligence (IJCAI 2016), Workshop
on Interactive Machine Learning: Connecting Humans and Machines, New York City, NY, USA, 9th-15th
July 2016.
Bibtex Entry:
@INPROCEEDINGS{ahmadzadeh2016encoding, TITLE={Encoding Demonstrations and Learning New Trajectories using Canal Surfaces}, AUTHOR={Ahmadzadeh, Seyed Reza and Chernova, Sonia}, BOOKTITLE={25th Inernational joint Conference on Artificial Intelligence ({IJCAI}), Workshop on Interactive Machine Learning: Connecting Humans and Machines}, PAGES={1--7}, YEAR={2016}, MONTH={July}, ADDRESS={New York City, NY, USA}, ORGANIZATION={{IEEE}} }
Abstract:
We propose a novel learning approach based on differential geometry to extract and encode important
characteristics of a set of trajectories captured through demonstrations. The proposed approach
represents the trajectories using a surface in Euclidean space. The surface, which is called Canal
Surface, is formed as the envelope of a family of regular implicit surfaces (e.g. spheres) whose
centers lie on a space curve. Canal surfaces extract the essential aspects of the demonstrations
and retrieve a generalized form of the trajectories while maintaining the extracted constraints.
Given an initial pose in task space, a new trajectory is reproduced by considering the relative
ratio of the initial point with respect to the corresponding cross-section of the obtained canal
surface. Our approach produces a continuous representation of the set of demonstrated trajectories
which is visually perceivable and easily understandable even by non-expert users. Preliminary
experimental results using simulated and real-world data are presented.

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