Reliability-Asymmetric Spacecraft Autonomy: Co-Designing a Capable Learned GNC Stack with a Verified, Adaptation-Aware Runtime Shield

• URL: http://arxiv.org/abs/2606.25366v1
• Date: Wed, 24 Jun 2026 03:55:21 GMT
• Title: Reliability-Asymmetric Spacecraft Autonomy: Co-Designing a Capable Learned GNC Stack with a Verified, Adaptation-Aware Runtime Shield
• Abstract summary: Rule-based autonomy is certifiable but brittle, while learned autonomy is capable but hard to verify.<n>We present AMPLE-GNC, a three-tier guidance, navigation, and control stack.<n>Fine-tuning a pretrained 360M model with grammar-constrained decoding gives a hard output-validity guarantee.<n>We show that a latching safe-hold shield can suppress even a capable controller.
• Score: 2.28438857884398
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep-space missions need onboard autonomy that is both capable and certifiable. Rule-based autonomy is certifiable but brittle, while learned autonomy is capable but hard to verify. We present AMPLE-GNC, a three-tier guidance, navigation, and control stack. Its capability path combines a small foundation-model commander that maps natural language to PDDL+, a constraint-screening verifier, and a fault-adaptive controller. All three are bounded by a runtime shield with nine linear-temporal-logic invariants whose predictor soundness is machine-checked by the Kind 2 model checker. On a 6-DOF Basilisk testbed, we make three contributions. First, we deploy an edge commander. Fine-tuning a pretrained 360M model with grammar-constrained decoding gives a hard output-validity guarantee and 84% planner-executable actions. On a de-leaked test, novel-phrasing generalization is 38% exact and 51% action, rising to 48% exact after phrasing-diversity re-finetuning; we separate syntactic validity from semantic accuracy. Second, we introduce a fault-adaptive controller. Rapid Motor Adaptation infers latent actuator faults online and recovers 97.8% of actuator-sign faults and 94.4% of continuous-gain faults within the training randomization envelope. Fault-unaware PD and from-scratch end-to-end RL both score 0%, while the strongest classical-adaptive baseline reaches 55% on continuous gain. Beyond the envelope, a split-conformant retrain scores 57-67%, and adding 4x more in-regime data worsens performance, showing that randomization breadth, not data volume, drives generalization. Robustness is flat under star-tracker noise to 0.005. Third, we show that a latching safe-hold shield can suppress even a capable controller. A split-conformal recovery-deadline certificate with adaptation-aware engagement reconciles safety and recovery, keeping the controller 94.5% autonomous while still catching non-recovery.

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