Vision-Based Learning from Demonstration System for Robot Arms Article Swipe
Pin-Jui Hwang
,
Chen‐Chien Hsu
,
Po‐Yung Chou
,
Wei‐Yen Wang
,
Cheng-Hung Lin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22072678
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22072678
Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and optimized when new tasks are to be deployed, resulting in detrimental downtime and high cost. It is therefore the objective of this paper to present a learning from demonstration (LfD) robotic system to provide a more intuitive way for robots to efficiently perform tasks through learning from human demonstration on the basis of two major components: understanding through human demonstration and reproduction by robot arm. To understand human demonstration, we propose a vision-based spatial-temporal action detection method to detect human actions that focuses on meticulous hand movement in real time to establish an action base. An object trajectory inductive method is then proposed to obtain a key path for objects manipulated by the human through multiple demonstrations. In robot reproduction, we integrate the sequence of actions in the action base and the key path derived by the object trajectory inductive method for motion planning to reproduce the task demonstrated by the human user. Because of the capability of learning from demonstration, the robot can reproduce the tasks that the human demonstrated with the help of vision sensors in unseen contexts.
Related Topics
Concepts
Artificial intelligence
Robot
Computer science
Downtime
Task (project management)
Key (lock)
Action (physics)
Object (grammar)
Human–computer interaction
Robot learning
Trajectory
Path (computing)
Computer vision
Mobile robot
Engineering
Systems engineering
Physics
Operating system
Quantum mechanics
Computer security
Astronomy
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22072678
- https://www.mdpi.com/1424-8220/22/7/2678/pdf?version=1648702461
- OA Status
- gold
- Cited By
- 12
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220700520
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4220700520Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22072678Digital Object Identifier
- Title
-
Vision-Based Learning from Demonstration System for Robot ArmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-31Full publication date if available
- Authors
-
Pin-Jui Hwang, Chen‐Chien Hsu, Po‐Yung Chou, Wei‐Yen Wang, Cheng-Hung LinList of authors in order
- Landing page
-
https://doi.org/10.3390/s22072678Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/7/2678/pdf?version=1648702461Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/7/2678/pdf?version=1648702461Direct OA link when available
- Concepts
-
Artificial intelligence, Robot, Computer science, Downtime, Task (project management), Key (lock), Action (physics), Object (grammar), Human–computer interaction, Robot learning, Trajectory, Path (computing), Computer vision, Mobile robot, Engineering, Systems engineering, Physics, Operating system, Quantum mechanics, Computer security, Astronomy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 6, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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