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Podcast: AI For Better Battery Design & Training Microrobots

In this episode, we talk about how AI is being leveraged for designing better batteries and training microswimmers.

Farbod Moghaddam

fromThe Next Byte

01 Mar, 2022.

Artist’s rendition of a particle analyzed by a combination of machine learning, X-ray and electron microscopy. (Image credit: Ella Maru Studio)

Topic

A.I.

EPISODE NOTES

(2:30) - AI & Imaging For Better Batteries

(12:30) - AI teaches microbots how to swim

Episode 59 was brought to you by Mouser Electronics, Farbod & Daniel’s favorite electronics distributor. Click here to read the article discussing the shortcomings of current battery technologies.

About the podcast:

Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell & Farbod Moghaddam who discuss the most interesting content in this podcast series.

To learn more about this show, please visit the shows page. By following the page, you will get automatic updates by email when a new show is published.

Be sure to give us a follow and review on Apple podcasts, Spotify, and most of your favorite podcast platforms!

AboutMouser Electronics

Mouser Electronics is a worldwide leading authorized distributor of semiconductors and electronic components for over 800 industry-leading manufacturers. We specialize in the rapid introduction of new products and technologies for design engineers and buyers. Our extensive product offering includes

Industry

Electronics

Headquarters

Mansfield, TX, USA

Founded

1964

Company Size

1001-5000

AboutThe Next Byte

The Next Byte Podcast is hosted by two young engineers - Daniel and Farbod - who select the most interesting tech/engineering content on Wevolver.com and deliver it in bite-sized episodes that are easy to understand regardless of your background. If you'd like to stay up to date with our latest ep

Industry

Entertainment

Headquarters

Washington D.C., DC, USA

Founded

2020

Image: Made to ease researchers' burdens, this microrobot control system trains itself.

Bio-inspired deep reinforcement learning teaches these smart magnetic microbots to swim

Designed for use in the human body for everything from drug delivery to less-invasive biopsies, these tiny microrobots operate under the control of a deep-learning system trained with no modelling or prior environmental knowledge.

artificial intelligence

23 Feb, 2022.

This article was discussed in our Next Byte podcast.

The full article will continue below.

Microrobots able to swim through fluid are of increasing interest in the medical field, capable of delivering precisely-targeted drugs, obtaining tissue samples, or even breaking up and removing blood clots. Actually controlling the devices once they’re inside the human body, though, remains a challenge — but one a pair of scientists from the University of Pittsburgh and Carnegie Mellon University may have found a way to solve.

Having developed simple magnetic microrobots, looking like tiny corkscrews, in a 3D-printed mold, the pair set about using deep reinforcement learning to create a system which could control their movement — without any manual training or simulation, and in fewer than 100,000 training steps.

The challenge

A tiny robot, easily fitting on the very tip of your finger, isn’t easy to control, and when you build it from complex composite materials — sometimes even integrating biological components — then set it loose in a complex environment like the human body, the challenge becomes near-intractable.

With too many variable, simulation is either entirely impossible or requires a range of sacrifices in the name of simplicity — potentially reducing the performance or capabilities of the finished control system. What Michael R. Behrens and Warren C. Ruder have proposed instead is to to do away with simulation altogether and create a control system based on deep reinforcement learning.

Inspired by natural learning, this system can drive swimming microrobots to a goal with no prior knowledge or modelling required.

Designed to mimic how real-world organisms approach learning, reinforcement learning aims to allow a deep learning system to adapt based on observations made about the sate of an envi