/Learning Sequential Composition Plans Using Reduced-Dimensionality Examples

Learning Sequential Composition Plans Using Reduced-Dimensionality Examples

Nicholas Melchior and Reid Simmons
Tech. Report, (AAAI) SS-09-01, Robotics Institute, Carnegie Mellon University, Papers from the 2009 AAAI Spring Symposium, March, 2009

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Programming by demonstration is an attractive model for allowing both experts and non-experts to command robots’ actions. In this work, we contribute an approach for learning precise reaching trajectories for robotic manipulators. We use dimensionality reduction to smooth the example trajectories and transform their representation to a space more amenable to planning. Next, regions with simple control policies are formed in the embedded space. Sequential composition of these simple policies is sufficient to plan a path to the goal from any known area of the configuration space. This algorithm is capable of creating efficient, collision-free plans even under typical real-world training conditions such as incomplete sensor coverage and lack of an environment model, without imposing additional requirements upon the user such as constraining the types of example trajectories provided. Preliminary results are presented to validate this approach.

Notes
This is not an RI tech. report, but AAAI workshops give publications tech. report numbers.

BibTeX Reference
@techreport{Melchior-2009-10182,
author = {Nicholas Melchior and Reid Simmons},
title = {Learning Sequential Composition Plans Using Reduced-Dimensionality Examples},
year = {2009},
month = {March},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {(AAAI) SS-09-01},
keywords = {Learning from Demonstration, Human Robot Interaction, Manipulation},
}
2017-09-13T10:41:16-04:00