New AI sees like a human, filling in the blanks

New AI sees like a human, filling in the blanks
Computer scientists at The University of Texas at Austin have taught an artificial intelligence agent how to do something that usually only humans can do--take a few quick glimpses around and infer its whole environment. Credit: Jenna Luecke/University of Texas at Austin.

Computer scientists at The University of Texas at Austin have taught an artificial intelligence agent how to do something that usually only humans can do—take a few quick glimpses around and infer its whole environment, a skill necessary for the development of effective search-and-rescue robots that one day can improve the effectiveness of dangerous missions. The team, led by professor Kristen Grauman, Ph.D. candidate Santhosh Ramakrishnan and former Ph.D. candidate Dinesh Jayaraman (now at the University of California, Berkeley) published their results today in the journal Science Robotics.

Most AI agents— that could endow robots or other machines with intelligence—are trained for very —such as to recognize an object or estimate its volume—in an environment they have experienced before, like a factory. But the agent developed by Grauman and Ramakrishnan is general purpose, gathering visual information that can then be used for a wide range of tasks.

"We want an agent that's generally equipped to enter environments and be ready for new perception tasks as they arise," Grauman said. "It behaves in a way that's versatile and able to succeed at different tasks because it has learned useful patterns about the visual world."

The scientists used , a type of machine learning inspired by the brain's neural networks, to train their agent on thousands of 360-degree images of different environments.

Now, when presented with a scene it has never seen before, the agent uses its experience to choose a few glimpses—like a tourist standing in the middle of a cathedral taking a few snapshots in different directions—that together add up to less than 20 percent of the full scene. What makes this system so effective is that it's not just taking pictures in random directions but, after each glimpse, choosing the next shot that it predicts will add the most new information about the whole scene. This is much like if you were in a you had never visited before, and you saw apples, you would expect to find oranges nearby, but to locate the milk, you might glance the other way. Based on glimpses, the agent infers what it would have seen if it had looked in all the other directions, reconstructing a full 360-degree image of its surroundings.

New AI sees like a human, filling in the blanks
A new AI agent developed by researchers at the University of Texas at Austin takes a few 'glimpses' of its surroundings, representing less than 20 percent of the full 360 degree view, and infers the rest of the whole environment. What makes this system so effective is that it's not just taking pictures in random directions but, after each glimpse, choosing the next shot that it predicts will add the most new information about the whole scene. Credit: David Steadman/Santhosh Ramakrishnan/University of Texas at Austin

"Just as you bring in prior information about the regularities that exist in previously experienced environments—like all the grocery stores you have ever been to—this agent searches in a nonexhaustive way," Grauman said. "It learns to make intelligent guesses about where to gather to succeed in perception tasks."

One of the main challenges the scientists set for themselves was to design an agent that can work under tight time constraints. This would be critical in a search-and-rescue application. For example, in a burning building a robot would be called upon to quickly locate people, flames and hazardous materials and relay that information to firefighters.

A video of active observation completion of unknown environments. Credit: Ramakrishnan et al., Sci. Robot. 4, eaaw6326 (2019)

For now, the new agent operates like a person standing in one spot, with the ability to point a camera in any direction but not able to move to a new position. Or, equivalently, the agent could gaze upon an object it is holding and decide how to turn the object to inspect another side of it. Next, the researchers are developing the system further to work in a fully mobile robot.

A video of sample walkthroughs of the reconstructed environments from the computer vision system’s egocentric point of view. Credit: Ramakrishnan et al., Sci. Robot. 4, eaaw6326 (2019)

Using the supercomputers at UT Austin's Texas Advanced Computing Center and Department of Computer Science, it took about a day to train their agent using an artificial intelligence approach called reinforcement learning. The team, with Ramakrishnan's leadership, developed a method for speeding up the training: building a second agent, called a sidekick, to assist the primary agent.

"Using extra information that's present purely during training helps the [primary] agent learn faster," Ramakrishnan said.


Explore further

Researchers develop new algorithms to train robots

More information: S.K. Ramakrishnan el al., "Emergence of exploratory look-around behaviors through active observation completion," Science Robotics (2019). robotics.sciencemag.org/lookup … /scirobotics.aaw6326
Journal information: Science Robotics

Citation: New AI sees like a human, filling in the blanks (2019, May 15) retrieved 23 May 2019 from https://techxplore.com/news/2019-05-ai-human-blanks.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
216 shares

Feedback to editors

User comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more