TaoGraph: Task-Oriented Scene Graph Learning for Embodied Planning

Embodied task planners are crucial for robotic navigation and manipulation. Among them, scene-graph-based approaches represent environments by semantically organizing objects into a spatial topology, supporting reasoning about object interactions and action prerequisites. However, constructing scene graphs that provide the spatial and procedural information needed for planning remains challenging. First, generic spatial representations lack task-aware adaptation, making it difficult to distinguish task-relevant spatial cues and construct relations needed for planning. Second, conventional graph supervision lacks sufficient procedural semantic guidance, making it difficult to infer action prerequisites and determine valid action orders. To address these challenges, we propose **TaoGraph**, a new task-oriented scene graph learning framework that transforms multi-view observations into task-oriented scene graphs through two key components. Semantics-Induced Spatial Reorganization (SISR) constructs task-conditioned spatial representations by modulating generic spatial features according to task instructions and scene semantics, preserving task-relevant information for graph generation. Change-Aware Procedure Distillation (CAPD) introduces procedure-guided graph supervision by distilling procedure-enriched graph predictions with guidance from discrepancies between two graph teachers, transferring procedural knowledge for action-order reasoning. Experiments demonstrate that SISR preserves task-relevant cues for graph reconstruction, while CAPD improves sequence-sensitive reasoning with comparable overall accuracy. Results on BLINK and MMSI-Bench further demonstrate TaoGraph's generalization to broader scene understanding and reasoning tasks.