arXiv Machine Learning By Alejandro Posadas-Nava, Richard Linares, Minduli Wijayatunga

Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations

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arXiv:2606. 17414v1 Announce Type: new Abstract: Autonomous spacecraft rendezvous and proximity operations (RPO) require controllers that guarantee safety under thrust constraints while minimizing fuel expenditure.

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arXiv Machine Learning
Jul 1

Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry

arXiv:2606. 31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure cases more effectively than traditional attitude control approaches.

By Alexander Fabisch, Melvin Laux, Mariela De Lucas \'Alvarez, Edoardo Caroselli, Julian Theis
arXiv Machine Learning
Sep 16

Neural Operator Learning for Collision-Aware Trajectory Planning of Spacecraft Swarms

The paper presents a permutation‑equivariant neural operator that learns to generate collision‑free, fuel‑efficient trajectories for spacecraft swarms by mapping distributions of initial and target states, as well as obstacle states, to trajectory outputs. The operator is self‑supervised and, when paired with a batched Gauss‑Newton step, enforces exact orbital dynamics and further reduces fuel consumption. Trained on ten spacecraft, the model generalizes zero‑shot to swarms of 1,000 spacecraft and 11,000 obstacles, achieving accuracy comparable to a per‑agent optimal control solver while maintaining collision avoidance.

By Sidhdharth D. Sikka, Suyi Gao, Zehui Lu, Rongjie Lai, Shaoshuai Mou
arXiv Machine Learning
Aug 4

Neural operator learning for collision-aware trajectory planning of spacecraft swarms

arXiv:2608. 00320v1 Announce Type: new Abstract: Autonomous spacecraft swarms must plan fuel-efficient, collision-free maneuvers in increasingly congested orbits, yet classical trajectory optimization scales poorly as pairwise safety constraints multiply with swarm size, and learning-based planners rarely transfer across swarm sizes or debris densities.

By Sidhdharth D. Sikka, Suyi Gao, Zehui Lu, Rongjie Lai, Shaoshuai Mou