Jul 2026· 55th International Conference on Environmental Systems· 0 citations
TL;DR
The results demonstrate the feasibility of transforming centralized ground support into a resilient, autonomous partner capable of safeguarding crew during high-latency planetary exploration.
Abstract
Future human exploration of Mars faces a critical operational barrier: communication latencies of 3 to 22 minutes will sever the real-time feedback loop between the crew and Earth-based Mission Control Center (MCC), rendering traditional “over-the-shoulder” guidance impossible during Extravehicular Activities (EVA). Building on our prior RASAGE (Retrieval & Simulation Augmented Guidance Agent for Exploration) work, we propose an onboard, multi-agent architecture that replicates key MCC console functions and their information pathways while operating under edge power, memory, and latency limits. This system decomposes the monolithic MCC structure into specialized agents, including the Flight Director, CAPCOM, and Systems Specialists, integrated with a Hybrid Retrieval-Augmented Generation with dedicated knowledge graph framework. To mitigate hallucination risks, the architecture employs deterministic tool use grounded in the NASA Crew State & Risk Model (CSRM), ensuring physiological validity and traceability to flight rules. System reasoning and orchestration are executed with Gemini 2.0 Flash to enable low-latency, long-context inference suitable for edge deployment, while evaluation uses Claude Opus 4.5 as an evidence-checking judge for long-horizon verification against source documentation. We validated the system by reconstructing the Apollo 14 EVA missions with a knowledge graph derived from over 50 historical mission documents. Across a benchmark of 244 operational queries, the system achieved an overall pass rate of 78.7%, with 94.6% accuracy on procedural inquiries and a hallucination rate below 0.4%. These results demonstrate the feasibility of transforming centralized ground support into a resilient, autonomous partner capable of safeguarding crew during high-latency planetary exploration.
The strong coupling among guidance laws, control loops, aerodynamics, and mission constraints poses growing challenges to the design of modern tactical missile guidance systems for autonomous flight. Conventional manual tuning and simulation-based trial-and-error result in long iteration cycles, limited reuse of design knowledge, and poor adaptability to changing scenarios. To address these limitations, we propose GSMultiAgent, a multi-agent collaborative cascade framework built atop Hermes Agent, which transforms natural-language mission requirements into optimized guidance system models through structured agent cooperation with feedback-driven iterative refinement. Three innovations are introduced: (1) a three-layer correction pipeline covering syntactic checking, deterministic mathematical verification, and semantic reasoning; (2) a bimodal experience repository supporting similarity-guided retrieval with access-count decay and best-quality retrieval for PPO warm-start initialization; and (3) a self-adjudicating optimizer that autonomously decides between PPO-based systematic parameter search and heuristic LLM-tuning guided by a reflection agent. Across four engagement scenarios, GSMultiAgent consistently attains high feasibility at a small fraction of the simulation budget required by conventional optimizers and single-agent baselines, and its design paths escalate autonomously from parameter tuning to structural law modification as task difficulty increases. Ablation studies confirm that the reflection agent, the optimization agent, and structured memory each contribute essential and complementary gains. These results establish multi-agent coordination with structured memory and self-adjudicating optimization as an effective paradigm for intelligent, reusable guidance system design.
This work represents the first concrete engineering step toward the embodied intelligent solar telescope concept, providing a validated foundation for the transition from automated scheduling to AI-driven autonomous observation.
Li-Yue Tong, Jiaben Lin, Yuanyong Deng et al.· Research in Astronomy and As...· 3 citations
GAIN-AI (Guided Assistant for Intelligent Navigation), a context-aware AI assistant and minimal heads-up interface for procedural guidance in simulated lunar EVA, is presented.
It is argued that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms.
Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al.· 0 citations
Long-horizon robotic tasks require diverse capabilities that no single policy can reliably provide. Heterogeneous policies offer complementary strengths, but orchestrating them requires reasoning over uncertain capability boundaries and cross-policy distribution mismatch, which are largely overlooked by existing planning methods built on homogeneous, predefined skills with fixed applicability. We propose RoboHarness, a unified framework that encapsulates independently developed robot control systems as reusable agentic skills. Although instantiated in this work with VLAs, RL policies, and task-and-motion planning (TAMP) systems, RoboHarness is designed as a general framework compatible with a broader range of robot policies, such as navigation policies, model predictive controllers, and world-action models. RoboHarness uses multi-modal execution memory and online evidence to characterize policy capability boundaries for capability-aware decomposition and routing. To stabilize policy handoffs, its Memory Bridge retrieves execution trajectories associated with the next policy, estimates its in-distribution state region, and guides the robot toward that region without joint policy retraining. Extensive experiments on three public benchmarks, 500 customized tasks, and 135 real-robot experiments demonstrate effective capability-aware routing and stable policy orchestration, yielding substantial improvements in zero-shot long-horizon planning and out-of-distribution robustness.
Jinbang Huang, Yuan Hu, Zhiyuan Li et al.· arXiv.org· 1 citation· ⚡1
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