Skip to Content
Artificial intelligence

AI could help make robots cheaper without limiting their abilities

July 19, 2019
An image of a developer holding a robotic hand
An image of a developer holding a robotic handHero Images/Getty

Transfer learning, the ability to use knowledge previously gained from one context in another, could teach cheap robots to perform as well as expensive ones.

The context: One of the hardest challenges currently facing robotics is getting the robot to operate smoothly outside the lab. In a research setting, it’s feasible to equip the robot with expensive sensors and provide it an ideal environment to learn navigation. But in the real world, using the same sensors would prove costly and unfriendly for consumers. Plus, it is messy and imperfect.

The proposal: Researchers at Vrije Universiteit turned to a type of machine learning known as transfer learning to see if they could solve the problem. Transfer learning is the process of taking what an algorithm has learned in one context and applying it in another. It could be used to adapt an algorithm that controls a robot in the lab so it can control a robot in the real world. That means the robot could first train with the advantage of better sensors and a better environment, and then exploit what it learned even when it only has cheap sensors and a poor environment.

The results: To test this idea, the researchers created a robot in a simulated environment that it navigated first with the aid of eight proximity sensors, and then with a single camera. They found that when the robot-controlling algorithm used transfer learning to make decisions—with camera access only—it learned to navigate around the room much faster than when it used no transfer learning at all. It was also much faster than when it used transfer learning during training rather than decision-making.

To have more stories like this delivered directly to your inbox, sign up for our Webby-nominated AI newsletter The Algorithm. It's free.

Deep Dive

Artificial intelligence

This new data poisoning tool lets artists fight back against generative AI

The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models. 

Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist

An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.

Unpacking the hype around OpenAI’s rumored new Q* model

If OpenAI's new model can solve grade-school math, it could pave the way for more powerful systems.

Generative AI deployment: Strategies for smooth scaling

Our global poll examines key decision points for putting AI to use in the enterprise.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.