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Intuitive Programming of Conditional Tasks by Demonstration of Multiple Solutions

Eiband, Thomas and Saveriano, Matteo and Lee, Dongheui (2019) Intuitive Programming of Conditional Tasks by Demonstration of Multiple Solutions. IEEE Robotics and Automation Letters, 4 (4), pp. 4483-4490. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LRA.2019.2935381. ISSN 2377-3766.

[img] PDF - Preprint version (submitted draft)

Official URL: https://ieeexplore.ieee.org/abstract/document/8798604


Conditional tasks include a decision on how the robot should react to an observation. This requires to select the appropriate action during execution. For instance, spatial sorting of objects may require different goal positions based on the objects properties, such as weight or geometry. We propose a framework that allows a user to demonstrate conditional tasks including recovery behaviors for expected situations. In our framework, human demonstrations define the required actions for task completion, which we term solutions. Each specific solution accounts for different conditions which may arise during execution. We exploit a clustering scheme to assign multiple demonstrations to a specific solution, which is then encoded in a probabilistic model. At runtime, our approach monitors the execution of the current solution using measured robot pose, external wrench, and grasp status. Deviations from the expected state are then classified as anomalies. This triggers the execution of an alternative solution, appropriately selected from the pool of demonstrated actions. Experiments on a real robot show the capability of the proposed approach to detect anomalies online and switch to an appropriate solution that fulfills the task.

Item URL in elib:https://elib.dlr.de/128977/
Document Type:Article
Title:Intuitive Programming of Conditional Tasks by Demonstration of Multiple Solutions
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Eiband, ThomasUNSPECIFIEDhttps://orcid.org/0000-0002-1074-9504UNSPECIFIED
Saveriano, MatteoUNSPECIFIEDhttps://orcid.org/0000-0002-9784-3973UNSPECIFIED
Lee, DongheuiUNSPECIFIEDhttps://orcid.org/0000-0003-1897-7664UNSPECIFIED
Date:14 August 2019
Journal or Publication Title:IEEE Robotics and Automation Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 4483-4490
EditorsEmailEditor's ORCID iDORCID Put Code
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Task analysis, Robot sensing systems, Monitoring, Force, Time series analysis, Switches, Learning from demonstration, failure detection and recovery, learning and adaptive systems
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Intuitive Human-Robot Interface [SY], Vorhaben Autonome, lernende Roboter (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Eiband, Thomas
Deposited On:18 Nov 2019 08:52
Last Modified:08 Nov 2023 08:17

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