What we Learned about Compliant Mechanical-neural-network Learning through Failure

Описание: This video details what we learned from our first and failed attempt at building a compliant mechanical neural network for the purpose of demonstrating the ability for an architected material to autonomously learn desired mechanical behaviors and properties. Although, the attempt failed and the machine never successfully learned any behaviors, it ironically taught us more about the learning process than the next improved design that did successfully demonstrate learning. Thus, this video explains all that was wrong with our first attempt and what improvements were necessary to enable a physical compliant mechanism to autonomously learn.

To better understand the contents of this more advanced video, it is recommended that you first familiarize yourself with the fundamental concepts by watching this seminal video titled, “Compliant mechanisms that LEARN! - Mechanical Neural Network Architected Materials.”

Video link:    • Compliant Mechanisms that LEARN! - Mechani...  

Also, we encourage you to read the following journal publication, which first introduced this content and can be accessed at the provided link:

Lee, R.H., Mulder, E.A.B., Hopkins, J.B., 2022, “Mechanical Neural Networks: Architected Materials that Learn Behaviors,” Science Robotics, 7(71): pp. 1-9
https://www.science.org/stoken/author...

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Acknowledgements:
Special thanks to Ryan Lee who built both mechanical neural networks featured in the video. I am also grateful to my AFOSR program officer, Byung “Les” Lee, who funded the research that this video features and was patient with our failures.

Disclaimer:
Responsibility for the content of this video is my own. The University of California, Los Angeles is not involved with this channel nor does it endorse its content.