SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants

Robots operating in dense horticulture settings such as pepper plants must perceive small, occluded plant structures while moving itself safely through the plant. We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and semantics-aware active view- motion planning. Beyond inspecting uncertain observed regions, the scene graph explicitly hypothesizes unobserved pepper–peduncle attachments and directs close-range sensing toward them. Candidate views are selected according to expected information gain, while class-dependent motion costs distinguish protected peppers, peduncles, and stems from conditionally traversable foliage. On pepper data, the perception network achieves 55.27\% semantic mIoU, 38.67\% PQ, and 40.62 mm depth RMSE, while input-conditioned uncertainty improves NYUv2 NLL from –1.6518 to –1.6925 and AUSE from 0.0102 to 0.0087.