Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey
Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to des...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/11/6/126 |
| _version_ | 1797455380737949696 |
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| author | Arturo Daniel Sosa-Ceron Hugo Gustavo Gonzalez-Hernandez Jorge Antonio Reyes-Avendaño |
| author_facet | Arturo Daniel Sosa-Ceron Hugo Gustavo Gonzalez-Hernandez Jorge Antonio Reyes-Avendaño |
| author_sort | Arturo Daniel Sosa-Ceron |
| collection | DOAJ |
| description | Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities. |
| first_indexed | 2024-03-09T15:53:36Z |
| format | Article |
| id | doaj.art-77a72d26f8d644d39e60db82ecc68bb2 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-09T15:53:36Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-77a72d26f8d644d39e60db82ecc68bb22023-11-24T17:50:19ZengMDPI AGRobotics2218-65812022-11-0111612610.3390/robotics11060126Learning from Demonstrations in Human–Robot Collaborative Scenarios: A SurveyArturo Daniel Sosa-Ceron0Hugo Gustavo Gonzalez-Hernandez1Jorge Antonio Reyes-Avendaño2School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, NL, MexicoHuman–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.https://www.mdpi.com/2218-6581/11/6/126human–robot collaborationrobot learninglearning from demonstrationsskill learning |
| spellingShingle | Arturo Daniel Sosa-Ceron Hugo Gustavo Gonzalez-Hernandez Jorge Antonio Reyes-Avendaño Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey Robotics human–robot collaboration robot learning learning from demonstrations skill learning |
| title | Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey |
| title_full | Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey |
| title_fullStr | Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey |
| title_full_unstemmed | Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey |
| title_short | Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey |
| title_sort | learning from demonstrations in human robot collaborative scenarios a survey |
| topic | human–robot collaboration robot learning learning from demonstrations skill learning |
| url | https://www.mdpi.com/2218-6581/11/6/126 |
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