Artificial Intelligence for Space

Expertise in AI for Space focuses on the design, development, and deployment of artificial intelligence methods for autonomous and intelligent space systems operating in complex, uncertain, and safety-critical environments.

This expertise spans AI-driven guidance, navigation, and control for satellites and spacecraft, enabling autonomous maneuvering, rendezvous and docking, formation flying, and on-orbit servicing. Core methods include reinforcement learning, model-based and hybrid learning approaches, optimal control, and data-driven decision making, with an emphasis on robustness, reliability, and adaptability under limited sensing, delayed feedback, and system faults.

A key component is the integration of computer vision and perception for space scenarios, such as pose estimation of non-cooperative objects, visual navigation, and environment understanding using monocular and multi-sensor imagery. These perception capabilities are tightly coupled with control and planning algorithms to enable closed-loop autonomy.

The expertise also covers simulation-driven development and validation, including high-fidelity physics environments, domain randomization, sim-to-real transfer, and continual learning to support long-duration missions. Strong attention is given to fault detection, fault-adaptive control, and resilience against model mismatch and unexpected dynamics.

Overall, AI for Space expertise bridges advanced AI theory and practical aerospace engineering, with the goal of delivering scalable, explainable, and deployable autonomous systems for next-generation space missions.