Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations
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. Input-constrained control barrier functions (ICCBFs) provide a control method for nonlinear systems with actuation constraints that construct a forward-invariant safe set. Previous work has shown that learning class-$\mathcal{K}$ functions defining the ICCBF recursion via meta reinforcement learning (meta-RL) yields a robust, non-greedy approach to safety-critical control in RPO. Thi
The increasing complexity and autonomy required for advanced space operations necessitate more robust and adaptive control systems, pushing the boundaries of AI in safety-critical contexts.
This research outlines a method for more reliable and fuel-efficient autonomous control in high-stakes space environments, directly impacting national security and strategic space capabilities.
The ability to dynamically adapt to adversarial conditions and guarantee safety in spacecraft proximity operations moves from theoretical to a more practical implementation using meta-RL.
- · Space agencies
- · Defense contractors
- · Satellite operators
- · AI/ML in aerospace
- · Traditional fixed-parameter control systems
- · Manual spacecraft control protocols
Enhanced autonomous capabilities for spacecraft maneuvering and orbital operations, reducing human intervention.
Accelerated development of on-orbit servicing, debris removal, and national security space missions due to increased reliability.
Potential for new doctrines in space warfare and orbital asset protection, as AI-driven autonomy becomes a core strategic advantage.
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Read at arXiv cs.LG