AI in Space

NASA is partnering with machine-learning researchers and computer companies on planetary defense and mapping the moon

3 min read
Machine learning is being used to produce more accurate maps of the moon's surface.
Among the extraterrestrial applications for which AI will be used is producing better maps of the moon's surface. Inadvertently steering a rover into one of the craters is far worse than driving over a pothole on the Brooklyn-Queens Expressway.
Image: Intel

If a distant comet is on course to collide with Earth, scientists will be able to detect it only about a year in advance. That doesn’t leave much time to prepare.

Artificial intelligence researchers believe they have the key to providing astronomers more foresight: machine learning algorithms that can more quickly identify and cluster the debris that comets leave in their wake. By speeding up analysis of meteor showers, researchers hope to pinpoint the orbits of distant, but potentially dangerous, comets. This project is one of five being explored as part of an artificial intelligence pilot research program sponsored by NASA.

Last Thursday at an event at Intel, participants in the NASA Frontier Development Laboratory research accelerators presented results showing how artificial intelligence can speed up space science. The lab, part of an effort by NASA to test the machine learning waters, is run by the SETI Institute; engineers at private companies including Intel, IBM, NVIDIA, and Lockheed Martin, among others, helped support the projects.

Companies such as Facebook and Google use machine learning to predict people’s buying habits and tag photos, but so far it hasn’t been widely applied to basic science problems, says Bill Diamond, CEO of the SETI Institute. Through Frontier Development Laboratory, which just finished its second year, NASA is exploring the possibilities. The lab sponsors small groups of computer and planetary science researchers to work on important problems in space science for two months each summer.

NASA scientists in the audience were excited, but skeptical, about the results from the comet detection project. Long-period comets, whose orbits take them far beyond Jupiter, are too distant to observe directly. What we can see is the evidence they leave in their wake. One type of clue is a meteor shower, which happens when Earth moves through debris left by a comet. Researchers on the comet project developed an image-classifying algorithm to more rapidly distinguish meteors from passing clouds, fireflies, and airplanes (a task that’s usually done by people) and then cluster these individual observations over time. In so doing, they were able to draw attention to a group of previously unidentified meteor showers. These showers, the group believes, may be evidence of previously undetected long-period comets.

The neural network, which the group put together and tested over the course of two months, agreed with human classifications of meteors about 90 percent of the time. In the pilot project, the group analyzed about one million meteors.

Some NASA reviewers in the audience wanted to see more evidence that the meteors detected by the neural network were not noise; others wanted more evidence that the meteors were actually from comets, not asteroids or other sources. Project scientist Marcelo de Cicco, an astronomer at the Brazilian National Metrology Institute, said there are many next steps to take. “We want to learn from what we can see, and look into these predicted orbits, because right now we have nothing,” he said.

Other projects had more to go on. One group used Intel’s deep-learning accelerator, called Nervana, to improve the resolution of maps of the moon. This team also used a neural net to classify images—crater or no crater? Their results agreed with human image classification about 98 percent of the time, about five times the accuracy of previous image analysis systems. The group’s aim is to provide recon so that future lunar rovers don’t fall into unmapped craters while looking for water at the moon’s poles. The poles are highly shaded, so it’s difficult to distinguish crater from shadow.

Two teams working on forecasting solar flares—magnetic pulses that can cause problems with the power grid, GPS, and other systems—had support from IBM and Lockheed Martin. One group’s algorithm, called FlareNET, outperformed NOAA’s existing system for predicting solar flares. “I don’t know who’s got the job of telling NOAA about this,” quipped Frontier Development Lab director James Parr.

“The projects show how AI can crunch the workflow, and do a few months of work in a few hours,” says Parr. Scientists in the room were excited about the prospects for continuing these projects beyond the pilot stage—and for putting the detection and forecasting systems into practice. However, neither Diamond nor Parr could comment on whether NASA will take up and expand on any of the projects before next summer’s session.

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How the U.S. Army Is Turning Robots Into Team Players

Engineers battle the limits of deep learning for battlefield bots

11 min read
Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

“I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

This article is part of our special report on AI, “The Great AI Reckoning.”

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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