Encoding Demonstrations and Learning New Trajectories using Canal Surfaces

Post date: Jul 01, 2016 3:8:48 PM

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.