In the vast, silent theater of space, a rare class of cosmic wanderers makes a fleeting appearance—interstellar objects (ISOs). These mysterious travelers are cosmic postcards from distant star systems, composed of primitive materials untouched by time. They barrel through our solar system like cosmic bullets, never to return, moving at mind-bending speeds of tens of kilometers per second. For scientists eager to unlock the secrets they carry, these objects offer both an unparalleled opportunity and a monumental challenge.
But what if we had a way to meet them head-on? To intercept these one-time visitors before they slip away into the dark once more? Enter Neural-Rendezvous, an innovative deep learning-powered system poised to revolutionize how we interact with these elusive messengers from other worlds.
Developed by Dr. Hiroyasu Tsukamoto, a faculty member in the Department of Aerospace Engineering at the Grainger College of Engineering, University of Illinois Urbana-Champaign, Neural-Rendezvous represents a bold leap forward. It’s not just an algorithm or a computer program—it’s a specialized artificial brain, meticulously trained and mathematically proven to autonomously guide spacecraft to encounter ISOs in the unpredictable vastness of space.
And the potential? Nothing short of groundbreaking.
The Cosmic Challenge: Intercepting Interstellar Interlopers
To appreciate the brilliance behind Neural-Rendezvous, it helps to understand the cosmic challenge it addresses. ISOs are some of the most difficult objects to study in modern astronomy. Unlike asteroids or comets bound to the sun, ISOs come from other star systems, flung into interstellar space by gravitational pinball games around their home stars. They are unbound, rogue objects on a one-way ticket through our solar system.
The problem? They’re fast. Unimaginably fast. And unpredictable.
When the first known ISO, ʻOumuamua, zipped through our solar system in 2017, it was already on its way out by the time telescopes locked onto it. Astronomers had mere weeks to observe it—and questions vastly outnumbered answers. Was it an asteroid? A comet? Or something stranger?
“We’re trying to encounter an astronomical object that streaks through our solar system just once, and we don’t want to miss the opportunity,” Tsukamoto explained.
But ISO encounters are unpredictable. We don’t know when or where the next one will appear. Planning an intercept mission in advance is like trying to hit a bullseye on a dartboard you can’t see, while blindfolded, as the dartboard moves at breakneck speed.
That’s why Tsukamoto’s Neural-Rendezvous isn’t just impressive. It’s essential.
Neural-Rendezvous: Teaching Spacecraft to Think
So, how does Neural-Rendezvous work? Tsukamoto likens it to a human brain, but one that specializes in a singular, high-stakes skill: rendezvousing with a rogue object in the depths of space.
“A human brain has many capabilities: talking, writing, etcetera,” Tsukamoto said. “Deep learning creates a brain specialized for one of these capabilities with domain-specific knowledge.”
Neural-Rendezvous is that brain. It’s a deep-learning-driven guidance and control system that enables spacecraft to make autonomous, intelligent decisions in real time. Unlike traditional spacecraft, which follow pre-programmed flight paths based on careful mission planning, Neural-Rendezvous trains spacecraft to react and adapt on the fly.
The system learns from massive datasets that simulate ISO encounters, enabling it to make predictions about the ISO’s position, speed, and trajectory. Then it calculates the optimal maneuver to get as close as possible—while ensuring safety, given the enormous energy and cost constraints of space missions.
It’s not just about chasing a needle in a cosmic haystack. Neural-Rendezvous brings something no other system has before: mathematical proof that its decisions will work.
“For example, with a human brain we learn from experience how to navigate safely while driving,” Tsukamoto explained. “But what are the mathematics behind it? How do we know and how can we make sure we won’t hit anyone?”
Neural-Rendezvous provides those guarantees. Its decision-making is backed by contraction theory, a mathematical framework Tsukamoto developed during his Ph.D. at Caltech. This ensures that, despite uncertainties and chaotic conditions, the spacecraft stays on course and doesn’t crash—no matter how unpredictable the ISO’s movements may be.
Why This Matters: Speed, Uncertainty, and Autonomy
ISO encounters aren’t like ordinary space missions. They’re chaotic, fast, and unforgiving.
First, ISOs move fast—tens of kilometers per second fast. Second, because their origins are unknown, their exact trajectories are hard to pin down. By the time we detect one, we’re already racing against the clock. Traditional mission planning, which takes years, isn’t an option.
That’s why Neural-Rendezvous takes a radically different approach. It gives spacecraft the autonomy to “think” and react in real time, after launch. It’s a brain that runs onboard, continuously evaluating and responding to sensor data to adjust its approach.
“Unlike traditional approaches in which you design almost everything before you launch a spacecraft, to encounter an ISO, a spacecraft has to have something like a human brain, specifically designed for this mission, to fully respond to data onboard in real time,” Tsukamoto said.
It’s artificial intelligence not just as a tool, but as an active pilot.
Swarming the Visitor: Multi-Spacecraft Solutions
One spacecraft? Great. A swarm of spacecraft? Even better.
At NASA’s Jet Propulsion Laboratory (JPL), where Tsukamoto spent time as a post-doctoral research affiliate, he worked with Illinois aerospace undergrads Arna Bhardwaj and Shishir Bhatta. Their project? Expanding Neural-Rendezvous into a multi-spacecraft framework, turning one hunter into an entire coordinated pack.
Because of the speed and uncertainty of ISOs, even the best AI brain can struggle to get a perfect read during a flyby. But with multiple spacecraft working together, the odds improve dramatically.
Their approach involved using M-STAR, a multi-spacecraft simulator, and Crazyflies, tiny drones that can replicate formation flying and cooperative control strategies. In these demonstrations, they showed how a swarm of spacecraft could be positioned to cover a wider area, maximizing the chance of capturing detailed data during an ISO flyby.
“Now we have an additional layer of decision-making during the ISO encounter,” Tsukamoto explained. “How do you optimally position multiple spacecraft to maximize the information you can get out of it?”
Their solution: Distribute the swarm to cover the most probable locations where the ISO might pass. Neural-Rendezvous guides each spacecraft in the swarm, ensuring they work together like a coordinated team rather than independent agents.
It’s the first step toward turning Neural-Rendezvous from a single-system concept into a full-blown fleet of interstellar object hunters.
Beyond the Theory: Toward Practical Missions
For now, Neural-Rendezvous remains largely theoretical. But its proof-of-concept demonstrations and rigorous mathematical backing have already opened doors. What began as Tsukamoto’s personal project has now evolved into a collaborative effort that could fundamentally change how we explore ISOs.
“And while the Neural-Rendezvous is more of a theoretical concept, their work is our first attempt to make it much more useful, more practical,” Tsukamoto said of Bhardwaj and Bhatta’s contributions.
The research has been published in the Journal of Guidance, Control, and Dynamics and shared on the arXiv preprint server. It’s catching attention, not just from academia but from space agencies and private space exploration companies that see the potential.
Because if we want to meet the next ʻOumuamua—or something even stranger—Neural-Rendezvous may be the only way to do it.
The Future: A New Age of Cosmic Exploration
The concept of Neural-Rendezvous opens exciting new frontiers. It’s not just about hunting ISOs for curiosity’s sake. These objects are time capsules, carrying materials from other star systems that could help us answer some of the biggest questions humanity has ever asked.
Where did we come from? How common is life in the universe? What’s the true nature of planetary systems beyond our own?
Tsukamoto’s work suggests we may soon have the tools to reach out and touch these cosmic messengers before they disappear forever. By equipping spacecraft with artificial intelligence that can think, adapt, and act autonomously, Neural-Rendezvous sets the stage for missions that were once impossible.
And as technology advances—miniaturized spacecraft, better propulsion systems, and improved AI—we could one day have swarms of spacecraft, guided by artificial brains, ready to greet these interstellar visitors at a moment’s notice.
For now, Neural-Rendezvous is a tantalizing glimpse into that future. A future where human ingenuity and artificial intelligence come together to chase down the mysteries that pass us by in the night.
References: Hiroyasu Tsukamoto et al, Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects, Journal of Guidance, Control, and Dynamics (2024). DOI: 10.2514/1.G007671
Arna Bhardwaj et al, Information-Optimal Multi-Spacecraft Positioning for Interstellar Object Exploration, arXiv (2024). DOI: 10.48550/arxiv.2411.09110