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Indian-origin scholar pioneers AI control on ISS
Researchers have demonstrated a machine-learning system that helped a robot aboard the ISS plan autonomous movements 50–60 percent faster.
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Rwittika Chakraborty
- Sun, 14, Dec 2025
- 00:00 EST
- 19
Somrita Banerjee / LinkedIn/Somrita Banerjee
Indian-origin researcher Somrita Banerjee at Stanford University has become the first to show that machine-learning-based control can operate aboard the International Space Station.
According to a Stanford report, astronauts increasingly rely on robots aboard the ISS, but many tasks have remained too complex and computationally demanding for the machines to perform autonomously.
New research, published in and presented at the 2025 International Conference on Space Robotics, introduces a system designed to help Astrobee, a cube-shaped, fan-powered robot, navigate the ISS on its own.
Researchers have now demonstrated a machine-learning system that helped the robot plan autonomous movements 50–60 percent faster, bringing AI-supported robotics to the ISS for the first time. The milestone marks an early step toward making such systems a routine part of future missions.
Banerjee, who conducted the work as part of her PhD, told Stanford,
“This is the first time AI has been used to help control a robot on the ISS. It shows that robots can move faster and more efficiently without sacrificing safety, which is essential for future missions where humans won’t always be able to guide them.”
Marco Pavone, associate professor of aeronautics and astronautics in the School of Engineering and director of Stanford’s Autonomous Systems Laboratory, said his lab will continue pursuing the research.
“As part of the Center for Aerospace Autonomy Research, we are collaborating with the Stanford Space Rendezvous Lab to explore more powerful AI models — the same kinds used in modern language tools and self-driving systems,” he told Stanford. “With stronger generalization capabilities, these models would enable robots to navigate even more challenging situations in future space missions.”
The research was funded by the Office of Naval Research, a NASA Early Stage Innovation grant and a NASA Space Technology Graduate Fellowship grant.
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