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Trajectory Learning from Demonstration with Canal Surfaces: A Parameter-Free Approach

posted Dec 8, 2016, 8:15 AM by Reza A   [ updated Jun 26, 2017, 1:50 PM ]
Seyed Reza Ahmadzadeh, Roshni Kaushik, Sonia Chernova

Seyed Reza Ahmadzadeh, Roshni Kaushik, Sonia Chernova, "Trajectory Learning from Demonstration with
Canal Surfaces: A Parameter-free Approach", In Proc. 16th IEEE-RAS Intl Conf. on Humanoid Robots
(Humanoids 2016), Cancun, Mexico, pp. 544--549, 15-17 Non. 2016.
Bibtex Entry:
@INPROCEEDINGS{ahmadzadeh2016trajectory, TITLE={Trajectory Learning from Demonstration with Canal Surfaces: A Parameter-free Approach}, AUTHOR={Ahmadzadeh, Seyed Reza and Kaushik, Roshni and Chernova, Sonia}, BOOKTITLE={Humanoid Robots ({H}umanoids), {IEEE-RAS} International Conference on}, YEAR={2016}, MONTH={November}, ORGANIZATION={IEEE}, PAGES={544--549}, ADDRESS={Cancun, Mexico}, DOI={10.1109/HUMANOIDS.2016.7803328} }
We present a novel geometric framework for intuitively encoding and learning a wide range of
trajectory-based skills from human demonstrations. Our approach identifies and extracts the main
characteristics of the demonstrated skill, which are spatial correlations across different
demonstrations. Using the extracted characteristics, the proposed approach generates a continuous
representation of the skill based on the concept of canal surfaces. Canal surfaces are Euclidean
surfaces formed as the envelope of a family of regular surfaces (e.g. spheres) whose centers lie
on a space curve. The learned skill can be reproduced, as a time-independent trajectory, and
generalized to unforeseen situations inside the canal while its main characteristics are preserved.
The main advantages of the proposed approach include: (a) requiring no parameter tuning,
(b) maintaining the main characteristics and implicit boundaries of the skill, and (c) generalizing
the learned skill over the initial condition of the movement, while exploiting the whole
demonstration space to reproduce a variety of successful movements. Evaluations using simulated and
real-world data exemplify the feasibility and robustness of our approach.

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