mlsanityData to 14 Oct 2026

Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint

arXiv 2605.19967 ↗First posted 19 May 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

This paper implements deep reinforcement learning (DRL) with a safety filter for spacecraft reorientation control with a single pointing keep-out zone. A new state space representation is designed which includes a compact representation of the attitude constraint zone. A reward function is formulated to achieve the control objective while enforcing the attitude constraint. The soft actor-critic (SAC) algorithm is adopted to handle continuous state and action space. A curriculum learning approach is implemented for agent training. To guarantee the compliance of the attitude constraint, a control barrier function (CBF)-based safety filter is implemented for agent deployment. Simulation results demonstrate the effectiveness of the proposed state space presentation and the designed reward function. Monte Carlo simulations underscore that reward shaping alone cannot guarantee the safety during reorientation maneuver. In contrast, with the CBF-based safety filter, the constraint can be guaranteed during maneuvers.

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 4 of its 25 references that are in the map. First posted on arXiv 19 May 2026.

Builds on

4 of 25 references.

References · 4

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  3. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 118
  4. 2019Safe Reinforcement Learning on Autonomous Vehiclescited by 50

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  4. 2025Deep Reinforcement Learning Policies for Underactuated Satellite Attitude Control
  5. 2026Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations
  6. 2026Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations
  7. 2026SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking
  8. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks