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Enke C, Auberle J (2022). Learning from Demonstration in Material Handling Processes. Logistics Journal : Proceedings, Vol. 2022. (urn:nbn:de:0009-14-55862)

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%0 Journal Article
%T Learning from Demonstration in Material Handling Processes
%A Enke, Constantin
%A Auberle, Jonathan
%J Logistics Journal : Proceedings
%D 2022
%V 2022
%N 18
%@ 2192-9084
%F enke2022
%X Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.
%L 620
%K Flexibility
%K Flexibilität
%K Material Handling Processes
%K Materialhandhabungsprozesse
%K Robotik
%K robotics
%R 10.2195/lj_proc_enke_en_202211_02
%U http://nbn-resolving.de/urn:nbn:de:0009-14-55862
%U http://dx.doi.org/10.2195/lj_proc_enke_en_202211_02
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@Article{enke2022,
  author = 	"Enke, Constantin
		and Auberle, Jonathan",
  title = 	"Learning from Demonstration in Material Handling Processes",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2022",
  volume = 	"2022",
  number = 	"18",
  keywords = 	"Flexibility; Flexibilit{\"a}t; Material Handling Processes; Materialhandhabungsprozesse; Robotik; robotics",
  abstract = 	"Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_proc_enke_en_202211_02",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-55862"
}
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TY  - JOUR
AU  - Enke, Constantin
AU  - Auberle, Jonathan
PY  - 2022
DA  - 2022//
TI  - Learning from Demonstration in Material Handling Processes
JO  - Logistics Journal : Proceedings
VL  - 2022
IS  - 18
KW  - Flexibility
KW  - Flexibilität
KW  - Material Handling Processes
KW  - Materialhandhabungsprozesse
KW  - Robotik
KW  - robotics
AB  - Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-55862
DO  - 10.2195/lj_proc_enke_en_202211_02
ID  - enke2022
ER  -
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<b:Comments>Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.</b:Comments>
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ISI

PT Journal
AU Enke, C
   Auberle, J
TI Learning from Demonstration in Material Handling Processes
SO Logistics Journal : Proceedings
PY 2022
VL 2022
IS 18
DI 10.2195/lj_proc_enke_en_202211_02
DE Flexibility; Flexibilität; Material Handling Processes; Materialhandhabungsprozesse; Robotik; robotics
AB Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.
ER
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Mods

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    <namePart type="given">Jonathan</namePart>
  </name>
  <abstract>Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes.</abstract>
  <subject>
    <topic>Flexibility</topic>
    <topic>Flexibilität</topic>
    <topic>Material Handling Processes</topic>
    <topic>Materialhandhabungsprozesse</topic>
    <topic>Robotik</topic>
    <topic>robotics</topic>
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Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2022, Iss. 18
Title

Learning from Demonstration in Material Handling Processes (eng)
Author Constantin Enke, Jonathan Auberle
Language eng
Abstract Processes in material handling must be flexible and easily adaptable. It is simple for a human to learn to grasp a box from a shelf. To teach a robot to do the same requires programming skills and therefore skilled personnel. Because of this the Learning from Demonstration (LfD) approach is gaining importance in recent years. A robot learns from a human demonstrating a task and then reproduces it in new situations. In the area of material handling many situations could benefit from the use of robots, but the implementation often fails because of complex programming or the lack of flexibility of the automated solutions. Therefore, a framework is presented, that is tailored to these specific requirements. The 5+5 Steps of the Material Handling Loop propose that most tasks in material handling can be segmented into simpler rules. Each of these tasks consist of picking up an object from a source, moving it to a sink and placing it down again. The flexibility of this approach was investigated in two experimental series. While there are still some short-comings and open issues, it is shown, that this framework enables adaptive and flexible applications for LfD in material handling processes. Prozesse in der Materialhandhabung müssen flexibel und leicht anpassbar sein. Ein Mensch kann einfach lernen, eine Kiste aus einem Regal zu greifen. Einem Roboter dasselbe beizubringen, erfordert Programmierkenntnisse und daher Fachpersonal. Aus diesem Grund gewinnt der Ansatz des Lernens von Demonstration (LfD) in letzter Zeit an Bedeutung. Der Roboter lernt von einem Menschen, der die Aufgabe vorführt, und reproduziert sie dann in neuen Situationen. Im Bereich der Materialhandhabung gibt es viele Situationen, die vom Einsatz von Robotern profitieren könnten, aber die Umsetzung scheitert oft an komplexer Programmierung oder mangelnder Flexibilität der automatisierten Lösungen. Daher wird ein Framework vorgestellt, das auf diese spezifischen Anforderungen zugeschnitten ist. Die 5+5 Schritte des Material-Handling-Loops basieren auf der Annahme, dass die meisten Aufgaben in der Handhabung in einfachere Regeln unterteilt werden können. Für jede Aufgabe muss ein Objekt in einer Quelle aufgenommen werden, zu einer Senke transportiert und dort wieder abgelegt werden. Die Flexibilität dieses Ansatzes wurde in zwei Versuchsreihen untersucht. Obwohl es noch Verbesserungspotentiale gibt, konnte gezeigt werden, dass dieses Framework adaptive und flexible Anwendungen für das LfD in Materialhandhabungsprozessen ermöglicht.
Subject Flexibility, Flexibilität, Material Handling Processes, Materialhandhabungsprozesse, Robotik, robotics
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-55862
DOI https://doi.org/10.2195/lj_proc_enke_en_202211_02