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A Geometric Approach for Encoding Demonstrations and Learning New Trajectories

posted Jun 19, 2016, 9:01 AM by Reza A   [ updated Jun 24, 2017, 9:43 AM ]
Seyed Reza Ahmadzadeh, Sonia Chernova

Reference:
Seyed Reza Ahmadzadeh, Sonia Chernova, "A Geometric Approach for Encoding Demonstrations and
Learning New Trajectories", In Proc. Robotics: Science and Systems (RSS 2016), Workshop on Planning
for Human-Robot Interaction: Shared Autonomy and Collaborative Robotics, Ann Arbor, Michigan, USA,
18-22 June 2016.
Bibtex Entry:
@INPROCEEDINGS{ahmadzadeh2016geometric, TITLE={A Geometric Approach for Encoding Demonstrations and Learning New Trajectories}, AUTHOR={Ahmadzadeh, Seyed Reza and Chernova, Sonia}, BOOKTITLE={Robotics: Science and Systems ({RSS}), Workshop on Planning for Human-Robot Interaction: Shared Autonomy and Collaborative Robotics}, PAGES={1--3}, YEAR={2016}, MONTH={June}, ADDRESS={Ann Arbor, Michigan, 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 called Canal Surface. The 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 a random
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 which is visually perceivable and easily
understandable even by non-expert users. Preliminary experimental results using real-world data are
presented.

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