Project CLOSR: Causal Lunar Operations & Sovereign Reasoning Engine

Abstract

An edge-native causal world model for autonomous planetary robotics, aimed at AMD’s Advancing AI 2026 Physical AI & Robotics track. CLOSR ingests high-resolution NASA LRO/LOLA lunar topography together with rover telemetry into a Spatio-Temporal Causal State Space Model that structurally separates passive environmental transitions from active robotic interventions. An on-board counterfactual query engine evaluates Pearl’s do-operator in real time, letting the agent isolate internal mechanical faults (e.g., motor-winding failure) from external constraints (e.g., regolith wheel slip) before actuating and propose safer alternative trajectories — all authored in PyTorch and optimized for low-latency local inference on AMD ROCm hardware. Full paper linked once it completes review.