Adaptive Science Operations in Deep Space Missions Using Offline Belief State Planning

Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations.

To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments.

We integrate a Bayesian network into the POMDP observation space to manage the high-dimensional and uncertain measurements typical of astrobiology missions. This network compactly encodes dependencies among measurements and improves the interpretability and computational tractability of science data. Instrument operation policies are computed offline, allowing resource-aware plans to be generated and thoroughly validated prior to launch.

We use the Enceladus Orbilander’s proposed Life Detection Suite (LDS) as a case study, demonstrating how Bayesian network structure and reward shaping influence system performance. We compare our method against the mission’s baseline Concept of Operations (ConOps), evaluating both misclassification rates and performance in off-nominal sample accumulation scenarios. Our approach reduces sample identification errors by nearly 40%

Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle, Zachary Booth, Edward Balaban, Mykel J. Kochenderfer

Comments: 7 pages, 4 tables, 5 figures, accepted in IEEE ISPARO 2026
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2510.08812 [cs.RO] (or arXiv:2510.08812v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2510.08812
Focus to learn more
Submission history
From: Grace Ra Kim
[v1] Thu, 9 Oct 2025 20:58:35 UTC (4,404 KB)
https://arxiv.org/abs/2510.08812

Astrobiology,