Distributed Generalization of Learned Planning Models in Robot Programming by Demonstration

Rainer Jaekel*, Pascal Meissner, Sven R. Schmidt-Rohr, Ruediger Dillmann

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

In Programming by Demonstration (PbD), one of the key problems for autonomous learning is to automatically extract the relevant features of a manipulation task, which has a significant impact on the generalization capabilities. In this paper, task features are encoded as constraints of a learned planning model. In order to extract the relevant constraints, the human teacher demonstrates a set of tests, e. g. a scene with different objects, and the robot tries to execute the planning model on each test using constrained motion planning. Based on statistics about which constraints failed during the planning process multiple hypotheses about a maximal subset of constraints, which allows to find a solution in all tests, are refined in parallel using an evolutionary algorithm. The algorithm was tested on 7 experiments and two robot systems.

Original languageEnglish
Title of host publication2011 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS
PublisherIEEE Press
Pages4633-4638
Number of pages6
Publication statusPublished - 2011
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - San Francisco, Canada
Duration: 25 Sep 201130 Sep 2011

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
CountryCanada
CitySan Francisco
Period25/09/1130/09/11

Cite this

Jaekel, R., Meissner, P., Schmidt-Rohr, S. R., & Dillmann, R. (2011). Distributed Generalization of Learned Planning Models in Robot Programming by Demonstration. In 2011 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (pp. 4633-4638). (IEEE International Conference on Intelligent Robots and Systems). IEEE Press.