This is like an autopilot for planning complex space missions. Instead of engineers manually trying thousands of possible flight paths, an AI learns how to string together many propulsion burns and gravity assists to find fuel‑efficient, fast routes through space.
Designing multi-phase spacecraft trajectories (e.g., multiple maneuvers, gravity assists, orbital transfers) is extremely complex, slow, and expert-intensive. This approach automates and accelerates trajectory design using transformer-based reinforcement learning so engineers can explore far more options in less time and with less fuel.
Domain-specific RL policy and training environment for multi-phase space trajectories, potentially combined with proprietary mission constraints, vehicle characteristics, and high-fidelity dynamics models.
Hybrid
Unknown
High (Custom Models/Infra)
Training stability and sample efficiency of RL in high-dimensional, multi-phase trajectory spaces; computational cost of simulating orbital dynamics and constraints at scale.
Early Adopters
Uses a transformer-based reinforcement learning architecture tailored to multi-phase spacecraft trajectory optimization, likely allowing end-to-end learning across many mission segments instead of hand-crafted heuristics or purely classical optimization.
4 use cases in this application