ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation

Coordinating navigation and manipulation with robust per-formance is essential for embodied AI in complex indoor environments.However, as tasks extend over long horizons, existing methods oftenstruggle due to catastrophic forgetting, spatial inconsistency, and rigidexecution. To address these issues, we propose ESCAPE (EpisodicSpatial memory Coupled with an Adaptive Policy for Execution), op-erating through a tightly coupled perception-grounding-execution work-flow. For robust perception, ESCAPE features a Spatio-Temporal FusionMapping module to autoregressively construct a depth-estimation-free,persistent 3D spatial memory, and a Memory-Driven Target Groundingmodule for precise interaction mask generation. To achieve flexible ac-tion, our Adaptive Execution Policy dynamically orchestrates proactiveglobal navigation and reactive local manipulation to seize opportunis-tic targets. ESCAPE achieves state-of-the-art performance on the AL-FRED benchmark, reaching 65.09% and 60.79% success rates in test seenand unseen environments with step-by-step instructions. By reducing re-dundant exploration, our ESCAPE substantially improves path-length-weighted metrics and remains robust (61.24%/56.04%) even without de-tailed guidance for long-horizon tasks.