In the movie WALL-E, the last Earthlings travel through the Kuiper Belt on board the starship Axiom. For 700 years, a fully automated crew of robots has cared for them now that Earth has become inhospitable. Running the ship’s operations is AUTO. AUTO is an artificial intelligence (AI) system working to keep humans away from Earth — forever.  

Here at home, space agencies like NASA and the European Space Agency are using AI to explore our solar system. This includes guiding rovers on Mars, preventing satellite collisions and training astronauts for spaceflight. But these AI systems are much more limited than their fictional counterparts.  

Today’s AI is much more unpredictable and prone to mistakes than what you see in fiction, says Daniele Gammelli. At Stanford University in California, he studies how to integrate AI systems into robots that interact with their environment.  

Anyone using ChatGPT has probably seen how it hallucinates, or makes up inaccurate information. AI systems in space robots would need to complete multi-step tasks in all sorts of scenarios without mistakes. After all, Gammelli says, in space, “you have virtually no room for error.”  

The little robot that could 

The title robot WALL-E is a trash-compacting machine — one that abandons his duties to follow another robot, EVE. WALL-E’s greatest strength is, arguably, his ability to handle change. To escape a self-destructing pod, WALL-E uses a fire extinguisher to flee. When his wheel or eye malfunctions, WALL-E can replace the damaged part. All this is learned from experience and done without additional programming.  

Such versatility is an example of artificial general intelligence (AGI). This type of AI doesn’t yet exist. It would think and learn like a human across different situations. AGI could also take on tasks for which it had not been programmed. 

Adapting to unforeseen situations is a big goal for future space robots, says Gammelli. Between radiation, extreme temperatures and space debris, space is a dangerous, ever-changing environment. Any robot navigating space would need to handle evolving conditions — many of which don’t exist on Earth.  

“The kind of scenarios you are kind of forcing on your robot are, by definition, things that nobody has ever seen,” he says. 

Today’s AI excels at single tasks or several closely related ones. Known as narrow AI, it handles repetitive and predictable work well. “I’d say the top skill that it excels in is processing a huge amount of data very efficiently,” says Sanjoy Paul in Houston, Texas. There, at Rice University, he studies how AI can assist in space missions. 

Martian rovers use narrow AI. To select rock samples, Perseverance employs AI algorithms to scan minerals and assess if they’re worth collecting. Another rover, Curiosity, uses AI to zap rocks with lasers based on their shape and color. All of this is done without human input.  

Any human sorting through that kind of data could get overwhelmed, says Paul. When faced with huge amounts of data, people can struggle to make decisions. Why? Picking out important details becomes much harder. “AI can actually cut through all the details … and highlight those things for humans to take a look at,” he says. 

To handle multi-step tasks, nearly all space robots rely on so-called autonomy stacks, explains Gammelli. These are separate, but linked, modules that tackle different actions. For instance, one AI model might use cameras or sensors to detect rocks or obstacles. This info would be passed to another module. It would interpret those results and determine the appropriate next action. Other modules would then carry out the physical maneuvers to get that job done.  

Still, today’s robots fall short of what people can physically do, says Paul. Many of the basic actions we perform require lots of coordination. Researchers are still studying how to design AI models to carry out actions as efficiently as possible, says Paul.  

Would you trust AI with your life? 

Aboard the fictional Axiom, robots manage everything. Custodial robots scrub and polish. Utility bots handle repairs and maintenance. Hover chairs cart the ship’s residents to their destinations. This lets Axiom’s passengers live sedentary lives, spending their days watching videos and drinking food shakes.  

People would probably need to play a bigger role in space travel than what was depicted in WALL-E, says Paul. While AI continues to improve by leaps and bounds, it remains unpredictable. With lives at risk, one small mistake could prove disastrous. “If your life depends on it, would you really bank on AI? Probably not,” he says. “You still need humans in the loop.” 

Most likely, he suspects, space travelers would rely on AI as an assistant. That might look more like another fictional AI system: J.A.R.V.I.S. from the Iron Man series. Tasked with monitoring Tony Stark’s buildings and power suits, J.A.R.V.I.S. acts more like a team member than a captain. It synthesizes data and offers suggestions to Stark, who remains in control. With large language models like ChatGPT and Claude, that option seems much more feasible, says Paul.  

Still, people wouldn’t want to micromanage all spacecraft, says Gammelli. Machines like rovers should eventually be able to make their own mini-goals, he says. These would all align with the engineer’s overall mission.  

Such mini-goals would allow bots to handle unforeseen situations better. It would also free up people to take care of more critical tasks and decisions. “We want these robots to be as independent as possible,” he says. Though maybe not as independent as mission-quitting WALL-E. 

While at Brown University in Providence, R.I., oceanographer Lily Dove studied the physics of Antarctica’s Southern Ocean with the help of some unusual, furry assistants: Antarctic seals. “They’re oceanographers,” she says. “They are regularly making dives in areas that we don’t often get to sample.”

Dove didn’t work with the seals directly. Instead, she interpreted data collected by the Marine Mammals Exploring the Oceans Pole to Pole program. This international group of scientists collects ocean data through sensors attached to the foreheads of southern elephant and Weddell seals. (Don’t worry. The animals don’t mind.) The seals dive more than 100 meters (330 feet) in search of food, allowing researchers to monitor temperature, salinity and pressure at depths inaccessible to human divers.

Dove used these data to study how climate change is impacting the Southern Ocean. That’s the vast ocean that circles Antarctica. In certain areas of the Southern Ocean, atmospheric heat moves from the surface to the deep ocean. This heat is typically trapped for hundreds of years once it reaches the depths, further increasing ocean temperatures.

Now at the Georgia Institute of Technology, Dove works with autonomous robots to study polar oceans. Some, such as floats, ride sea currents. Others, such as gliders, are pilotable underwater vehicles, meaning Dove can target specific locations. “And this is all remote,” she says. “I can sit here in Georgia and tell my glider in a whole different hemisphere where I want it to go.” 

This tech is also more predictable than her past assistants. “Seals are very independent,” she says. Unlike a glider, “seals very much tell me where they want to go,” she says. In this interview, Dove shares her experience and advice with Science News Explores. (This interview has been edited for content and readability.)

What inspired your career? 

Growing up in Florida, the ocean interested me as this powerful system that we take for granted. But I didn’t really consider it as a career back then. I knew I loved science, like physics and chemistry. In college, I knew I wanted to do one of those things or some combination. I changed majors like five times.

Then I realized that I liked those topics because they could help explain what I cared about: the Earth and the environment. I learned that you need all different kinds of science to understand the Earth system. Biology, physics, chemistry, geology — all of them. I ended up researching atmospheric chemistry.

How did you get to where you are today? 

In graduate school, I took those skills from studying atmospheric chemistry and started doing oceanography instead. The atmosphere and ocean have the same physics. That made it feel like it was the perfect transition for me. I landed in a group that was investigating polar oceans, which wasn’t something I’d even considered before. I went to the Southern Ocean for the first time in 2020. The polar oceans are pretty remote and far away from where most people live. But they have massive impacts on our climate system, especially the Southern Ocean. It wasn’t until I was there that I realized that. That’s when I knew that this is exactly what I wanted to be doing.

What was your biggest challenge, and how did you overcome it? 

I’ve experienced quite a bit of discrimination in this field as a woman. There are a lot of assumptions people have about who is welcome to do polar science. Historically, women have not been included on research cruises. Women used to stow away on ships to get data for their research theses. There’s a famous case at the Woods Hole Oceanographic Institution of a woman stowing away on a research vessel in the 1950s.

That stigma is still present to an extent, but it’s changing a lot. And it’s not just about women. People of color, queer people and disabled individuals are also experiencing this less-overt discrimination.

Throughout my time in the field and in academia, there have been times when people have assumed I can’t do something. That’s really frustrating, but it’s also given me the skill to really speak up and advocate for myself.

What advice would you give to your younger self? 

Say “yes” to as many opportunities as possible. That could be attending an event, talking with a scientist or taking a course. The worst-case scenario is that you learn something new that’s just not super interesting to you. The best-case scenario is that something grabs your attention, and you can’t let it go.

I am also glad that I didn’t hyperfixate on a particular area of study super early. I’m glad I got a strong basis in a lot of different scientific fields before I decided on a focus. It allowed me to be really flexible and open to new ideas.

If you already know that you’d want to do something specific, listen to your gut. But if you are not quite sure, lean into just learning a lot.