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Front. Robot. AI | doi: 10.3389/frobt.2018.00132

Trajectory-based Skill Learning using Generalized Cylinders

  • 1Georgia Institute of Technology, United States
  • 2School of Interactive Computing, College of Computing, Georgia Institute of Technology, United States

In this article, we introduce Trajectory Learning using Generalized Cylinders (TLGC) a novel trajectory-based skill learning approach from human demonstrations. To model a demonstrated skill, TLGC uses a Generalized Cylinder -- a geometric representation composed of an arbitrary space curve called the spine and a surface with smoothly varying cross-sections. Our approach is the first application of Generalized Cylinders to manipulation and its geometric representation, offers several key features: it identifies and extracts the implicit characteristics and boundaries of the skill by encoding the demonstration space, it supports for generation of multiple skill reproductions maintaining those characteristics, the constructed model can generalize the skill to unforeseen situations through trajectory editing techniques, our approach also allows for obstacle avoidance and interactive human refinement of the resulting model through kinesthetic correction. We validate our approach through a set of real-world experiments with a Jaco 6-DOF robotic arm.

Keywords: Learning from demonstration, Trajectory-based skill, robot learning, human-robot interaction, robot learning by demonstration

Received: 30 Jul 2018; Accepted: 27 Nov 2018.

Edited by:

Adriana Tapus, ENSTA ParisTech École Nationale Supérieure de Techniques Avancées, France

Reviewed by:

David Filliat, ENSTA ParisTech École Nationale Supérieure de Techniques Avancées, France
François Ferland, Université de Sherbrooke, Canada
Alan R. Wagner, Pennsylvania State University, United States  

Copyright: © 2018 Chernova and Ahmadzadeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Sonia Chernova, Georgia Institute of Technology, Atlanta, United States,