A Geometric Approach for Encoding Demonstrations and Learning New Trajectories

Post date: Jun 19, 2016 4:1:59 PM

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.