Fine-grained object (FO) manipulation requires robots to distinguish a specified FO from visually similar objects and execute actions reliably despite scene distractors. However, scene-level visual conditioning lacks explicit object selection, while category-level guidance cannot reliably distinguish FOs within the same category. We present a SAM3-guided visuomotor policy framework that addresses these challenges through persistent object memory and focused visual conditioning. First, we introduce FO Memory-driven SAM3 (FOM-SAM3), which learns reusable FO memory tokens from limited multi-view registration images, while keeping SAM3 fully frozen. Through one-vs-rest learning, these tokens encode persistent memories for localizing target FOs and rejecting similar alternatives, which can be stored in a memory bank for reuse. Second, we propose Focused Spatial-Appearance Encoding (FSAE), which combines in-FO appearance features with explicit bounding boxes to condition action policies, including Diffusion Policy (DP) and Action Chunking with Transformers (ACT). The effectiveness of the proposed FOM-SAM3 is validated on the FO-30 dataset comprising 30 physical objects across four coarse categories. Across three real-robot FO manipulation tasks, our FOM-SAM3-guided policies demonstrated robustness against distractors, discriminative ability of similar FOs, and extendibility to new FOs.

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