When space exploration began in the 1960s, it was a matter of the highest government secrecy. The U.S. and Soviet space programs were, in part, extensions of the Cold War, and their operations were accordingly confidential. As tensions cooled, the U.S. and Russia (along with other countries) began to collaborate on space missions.

Today, NASA’s Jet Propulsion Laboratory (JPL) is trying to democratize space travel and innovation even further. For instance, JPL now hopes to get ideas for its next generation of spacecraft from anyone, anywhere in the world by open-sourcing software and hardware. One example is the Open Source Rover, which looks like—and uses design features of—the current Mars Exploration Rover and is designed as a teaching tool.

The open-source rover project, created by JPL in 2018, made the design plans for a six-wheel rover based on the Mars rover available via GitHub, allowing people to build their own rovers and share them with other developers. Primarily designed to stoke interest in the U.S. space program and inspire the next generation of JPL scientists, it has also delivered unexpected dividends.

“What we didn’t anticipate is that it would become an innovation platform,” says Tom Soderstrom, JPL’s IT Chief Technology and Innovation Officer, of the open-source rover project.

The growing enthusiasm for robotics from a wide range of participants—from teenagers to PhDs—has resulted in innovation (creations include a robotic arm and a machine learning-powered rover) not only for JPL, but also for the future of robotics in general.

Machine Learning for the Masses

With the Open-Source Rover Project, JPL allows users to tinker with its hardware and software—and that means, in some cases, new technologies poured into the project are now available to the masses.

Amazon Web Services (AWS) engineer Jeremy Wallace, for example, used the Open Source Build-it-Yourself Rover project along with AWS RoboMaker to create a virtual testing ground for his own version of a rover equipped with a camera. RoboMaker extends the popular Robot Operating System (also known as ROS, a flexible framework for writing robotics software) with connectivity to cloud services, helping Wallace leverage massive machine learning running in the cloud to add a computer vision component to his rover. What’s more, by making his project available through open source on GitHub, Wallace set the stage to open up the possibility of using machine learning through robotics to a larger segment of developers, hobbyists, and tinkerers taking part in JPL’s Open-Source Build-it-Yourself Rover project.

The AWS project is a sort of video game where people can drive the rover through a simulated Mars environment, hunting for Martians. The image recognition, (a subset of machine learning) powered by Amazon Rekognition (AWS’ computer vision service) built into the application, identifies different objects in the simulated space and classifies them in real time.

“By having a service in the Cloud conduct the image recognition, in this case Amazon Rekognition, you significantly lower the cost of the rover computer,” says Wallace. “You can have a powerful machine learning program running the robot from the Cloud, instead of having to buy an expensive onboard computer for the robot.”

Amazon Rekognition makes advanced image and video analysis accessible to all developers and enthusiasts without requiring machine learning expertise to use. And Amazon RoboMaker allows robot developers to quickly test their robot and machine learning code before ever having to make the physical robot.

Wallace has open sourced both the code and 3D models available on GitHub so anyone can build their own rovers with computer vision readily and extend it to look for more advanced things, such as evidence of life and water.

Robot Rock Collection

The video-game-like creation from the AWS team has real-world implications, too.

Since its inception, deep space exploration has relied on the coordination of astronauts in the field and engineers back on Earth. But machine learning promises to allow the next generation of Mars rovers to work more autonomously—as it will power autonomy across industries.

In the case of space exploration, the current model of identifying and collecting samples on Mars is made by JPL engineers monitoring the rover from Earth. It’s possible that in the near future, JPL can develop a way for rovers to conduct this decision-making on their own—as evidenced by Wallace’s work. Today, when the real Mars rovers encounter a rock of interest, they can use artificial intelligence to decide to collect a sample without any human assistance. Having this new open source capability would allow citizen scientists to help improve and advance this capability.

“Innovations like this will drastically cut down on the amount of manpower needed to operate a rover and the amount of electricity a rover uses. But the greatest developments are yet to come,” Soderstrom says. Machine learning, while incredibly powerful, is still a relatively new technology to many people. And now that it’s widely available to the public, via the Open Source Build-it-Yourself Rover project for one, the potential for innovation is endless.