About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Developing Autonomous Spray Processes with Deep Reinforcement Learning Guided by Human Demonstration |
Author(s) |
Ezra Ameperosa, Anesia Auguste, Jennifer Ruddock, Erick Braham, James Hardin, Andrew Gillman |
On-Site Speaker (Planned) |
Andrew Gillman |
Abstract Scope |
Deep Reinforcement Learning (DRL) has recently shown promise for agile robotic control in real world applications but is often impractical due to the amount of data required and expensive trial-and-error learning. In principle, providing foresight to these algorithms through expert demonstration helps bridge developing control policies for otherwise difficult to automate tasks. We investigate this concept for applying speckle-patterns for Digital Image Correlation, a process known for its reliance on the expert knowledge and skills. Here, we consider the use of DRL in a custom spraying game/simulation and ways to efficiently accelerate learning with human demonstrations. We examined various methods for capturing information from an ‘expert’ performing the task, then how ‘expert’ data is infused into training to boost speed and performance of learning. We further aim to generalize these strategies with the potential of improving the efficiency of a more diverse range of complex manufacturing processes. |
Proceedings Inclusion? |
Undecided |