DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Online task segmentation by merging symbolic and data-driven skill recognition during kinesthetic teaching

Eiband, Thomas and Liebl, Johanna and Willibald, Christoph and Lee, Dongheui (2023) Online task segmentation by merging symbolic and data-driven skill recognition during kinesthetic teaching. Robotics and Autonomous Systems, 162, p. 104367. Elsevier. doi: 10.1016/j.robot.2023.104367. ISSN 0921-8890.

[img] PDF - Published version

Official URL: https://www.sciencedirect.com/science/article/pii/S0921889023000064


Programming by Demonstration (PbD) is used to transfer a task from a human teacher to a robot, where it is of high interest to understand the underlying structure of what has been demonstrated. Such a demonstrated task can be represented as a sequence of so-called actions or skills. This work focuses on the recognition part of the task transfer. We propose a framework that recognizes skills online during a kinesthetic demonstration by means of position and force-torque (wrench) sensing. Therefore, our framework works independently of visual perception. The recognized skill sequence constitutes a task representation that lets the user intuitively understand what the robot has learned. The skill recognition algorithm combines symbolic skill segmentation, which makes use of pre- and post-conditions, and data-driven prediction, which uses support vector machines for skill classification. This combines the advantages of both techniques, which is inexpensive evaluation of symbols and usage of data-driven classification of complex observations. The framework is thus able to detect a larger variety of skills, such as manipulation and force-based skills that can be used in assembly tasks. The applicability of our framework is proven in a user study that achieves a 96% accuracy in the online skill recognition capabilities and highlights the benefits of the generated task representation in comparison to a baseline representation. The results show that the task load could be reduced, trust and explainability could be increased, and, that the users were able to debug the robot program using the generated task representation.

Item URL in elib:https://elib.dlr.de/194567/
Document Type:Article
Title:Online task segmentation by merging symbolic and data-driven skill recognition during kinesthetic teaching
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Liebl, JohannaTechnical University of MunichUNSPECIFIEDUNSPECIFIED
Willibald, ChristophUNSPECIFIEDhttps://orcid.org/0000-0003-3579-4130UNSPECIFIED
Lee, DongheuiUNSPECIFIEDhttps://orcid.org/0000-0003-1897-7664UNSPECIFIED
Date:20 January 2023
Journal or Publication Title:Robotics and Autonomous Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:p. 104367
Keywords:Learning from demonstration Programming by demonstration Robot Symbolic Subsymbolic Data-driven Task segmentation Action segmentation Skill recognition Task representation Interactive robot programming Intuitive robot programming Force-based Tactile Online segmentation Kinesthetic teaching
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Autonomous learning robots [RO]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Management
Deposited By: Geyer, Günther
Deposited On:31 Mar 2023 12:11
Last Modified:31 Mar 2023 12:11

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.