Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations
On the map: Offline RL and imitation learning from data → Safe offline RL
Abstract (arXiv)
This paper presents a safety-guaranteed, runtime-efficient imitation learning framework for spacecraft close proximity control. We leverage Control Barrier Functions (CBFs) for safety certificates and Control Lyapunov Functions (CLFs) for stability as unified design principles across data generation, training, and deployment. First, a nonlinear Model Predictive Control (NMPC) expert enforces CBF constraints to provide safe reference trajectories. Second, we train a neural policy with a novel CBF-CLF-informed loss and DAgger-like rollouts with curriculum weighting, promoting data-efficiency and reducing future safety filter interventions. Third, at deployment a lightweight one-step CBF-CLF quadratic program minimally adjusts the learned control input to satisfy hard safety constraints while encouraging stability. We validate the approach for ESA-compliant close proximity operations, including fly-around with a spherical keep-out zone and final approach inside a conical approach corridor, using the Basilisk high-fidelity simulator with nonlinear dynamics and perturbations. Numerical experiments indicate stable convergence to decision points and strict adherence to safety under the filter, with task performance comparable to the NMPC expert while significantly reducing online computation. A runtime analysis demonstrates real-time feasibility on a commercial off-the-shelf processor, supporting onboard deployment for safety-critical on-orbit servicing.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 11 of its 23 references that are in the map. First posted on arXiv 19 Mar 2026.
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- 2019Control Barrier Functions: Theory and Applicationscited by 372
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
- 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 26
- 2023Transformers for Trajectory Optimization with Application to Spacecraft Rendezvouscited by 16
- 2024Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modelingcited by 16
- 2025Safe Physics-Informed Machine Learning for Dynamics and Controlcited by 9
- 2021Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectoriescited by 8
- 2022Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robotscited by 3
- 2024Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governorcited by 2
- 2024MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibilitycited by 1
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- 2026Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning
- 2026Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations
- 2026Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
- 2020Learning Control Barrier Functions from Expert Demonstrations
- 2025Explicit Control Barrier Function-based Safety Filters and their Resource-Aware Computation
- 2026Is Your Safe Controller Actually Safe? A Critical Review of CBF Tautologies and Hidden Assumptions
- 2026Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint
- 2026Memory-Efficient Meta-Reinforcement Learning for Adaptive Safety-Critical Control in Adversarial Spacecraft Proximity Operations