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

Post date: Dec 08, 2016 4:15:27 PM

Seyed Reza Ahmadzadeh, Roshni Kaushik, Sonia Chernova

Reference:

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} }

DOI:

10.1109/HUMANOIDS.2016.7803328

Abstract:

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.