Mimir is a neuro-symbolic memory system for long-horizon embodied tasks under partial observability. It separates world memory, which stores object locations, states, and perceptual evidence, from task memory, which tracks the ordered goal agenda, progress, hand state, failures, and execution constraints. Before each action, a grounding module connects the active goal to recalled world candidates, fills missing source locations, and attaches evidence for planning and execution. The paper reports maximum gains of 42.5% on EB-ALFRED and 23.0% on EB-Habitat, an 8.5% overall success-rate improvement over prior systems under the same backbone, and 86.0% success on the EB-Habitat long-horizon subset.
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