From Space to Factory
Our Inspiration
Applying AI that decides — not just analyzes — in real industrial environments is one of today’s most demanding technological frontiers. The complexity of these environments lies not only in the volume of data or the need for prediction, but in autonomous decision-making under hard constraints, time pressure, and operational uncertainty.
This Challenge isn´t unique to Earth
For decades, space agencies like NASA have developed autonomous systems designed to operate in extreme, unsupervised conditions — where every decision is mission-critical. One of the most iconic examples is CLARAty (Coupled Layer Architecture for Robotic Autonomy), developed by the Jet Propulsion Laboratory (JPL). This architecture enables planetary rovers to plan and execute actions autonomously, managing physical constraints, sensory uncertainty, and adapting in real time to terrain. (JPL, ISAIRAS 2001)
NASA has also pioneered autonomous planning systems like ASPEN and MAPGEN, which use constraint programming and dynamic scheduling to make decisions during missions where communication delays with Earth span several minutes. In these systems, AI doesn’t suggest options — it decides what to do, when, and how. More recently, reinforcement learning under partial observability has been explored for autonomous collision avoidance in orbit — where satellites must react in real time to impact threats, based on uncertain data and without human intervention. (arXiv, 2023)
What does this mean for Aurora?
These space systems share a core principle: Intelligence isn’t outside the system. It’s embedded. And it decides.
Aurora is built on that same foundation. It adapts proven space-grade principles — constraint-based decision-making, autonomous learning, and uncertainty-aware execution — to real industrial environments, integrating directly into the operational stack.
The result: an embedded, optimized, and autonomous AI that doesn’t just predict — it acts. In real time. Without disrupting existing architecture. And always under human control.
Just as space exploration cannot rely on remote operators for every critical decision, the industry of the future cannot depend solely on post-facto analysis. It needs systems that decide.
