Computer Aided Design of Self-Learning Robotic System using Imitation Learning
Conference paper
Jadeja, Y., Shafik, M. and Wood, P. 2022. Computer Aided Design of Self-Learning Robotic System using Imitation Learning. Shafik, M. and Case, K. (ed.) Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research. IOS Press. https://doi.org/10.3233/ATDE220564
| Authors | Jadeja, Y., Shafik, M. and Wood, P. |
|---|---|
| Type | Conference paper |
| Abstract | Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Off course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. |
| Keywords | Imitation learning ; Self-learning robotic arm ; CNN; Resnet-50 ; Relu |
| Year | 2022 |
| Conference | Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research |
| Publisher | IOS Press |
| Digital Object Identifier (DOI) | https://doi.org/10.3233/ATDE220564 |
| Web address (URL) | https://ebooks.iospress.nl/doi/10.3233/ATDE220564 |
| Publisher's version | License File Access Level Open |
| ISBN | 978-1-64368-331-7 |
| Output status | Published |
| Publication dates | |
| Online | 2022 |
| Publication process dates | |
| Deposited | 13 Mar 2024 |
| Editors | Shafik, M. and Case, K. |
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