mlsanityData to 14 Oct 2026

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

arXiv 2606.17414 ↗First posted 16 Jun 2026cs.LG math.DScited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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-K functions defining the ICCBF recursion via meta reinforcement learning (meta-RL) yields a robust, non-greedy approach to safety-critical control in RPO. This paper extends that framework further by investigating the performance of three recurrent network architectures (Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Selective State Space Model (Mamba)) and two training algorithms (Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC)) to identify the best setup for tuning ICCBF class-K functions via meta-RL. In addition to cooperative test cases, performance is evaluated in the presence of adversarial behavior where the target spacecraft behaves in a way that worsens the safety of the chaser spacecraft. Results indicate that state space models such as Mamba when used with PPO achieve superior task completion, safety, and fuel-savings compared to other architectures, across all cooperative and uncooperative scenarios tested.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 30 references that are in the map. First posted on arXiv 16 Jun 2026.

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16 of 30 references.

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Same direction, newest first

  1. 2026Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations7 Feb
  2. 2026Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning30 Jan

Other references · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  4. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
  5. 2017Safe Reinforcement Learning via Shieldingcited by 449
  6. 2019Control Barrier Functions: Theory and Applicationscited by 372
  7. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 151
  8. 2023A Tutorial on Meta-Reinforcement Learningcited by 108
  9. 2021Safe Control Synthesis via Input Constrained Control Barrier Functionscited by 13
  10. 2019Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learningcited by 6
  11. 2021Control Barrier Functions in Sampled-Data Systemscited by 6
  12. 2022Design and Guidance of a Multi-Active Debris Removal Missioncited by 3
2 more
  1. 2022Exploiting Scaling Constants to Facilitate the Convergence of Indirect Trajectory Optimization Methodscited by 2
  2. 2026An Autonomous, End-to-End, Convex-Based Framework for Close-Range Rendezvous Trajectory Design and Guidance with Hardware Testbed Validationcited by 1

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