{"ID":2872367,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19318","arxiv_id":"2509.19318","title":"Scensory: Real-Time Robotic Olfactory Perception for Joint Identification and Source Localization","abstract":"While robotic perception has advanced rapidly in vision and touch, enabling robots to reason about indoor fungal contamination from weak, diffusion-dominated chemical signals remains an open challenge. We introduce Scensory, a learning-based robotic olfaction framework that simultaneously identifies fungal species and localizes their source from short time series measured by affordable, cross-sensitive VOC sensor arrays. Temporal VOC dynamics encode both chemical and spatial signatures, which we decode through neural networks trained on robot-automated data collection with spatial supervision. Across five fungal species, Scensory achieves up to 89.85% species accuracy and 87.31% source localization accuracy under ambient conditions with 3-7s sensor inputs. These results demonstrate real-time, spatially grounded perception from diffusion-dominated chemical signals, enabling scalable and low-cost source localization for robotic indoor environmental monitoring.","short_abstract":"While robotic perception has advanced rapidly in vision and touch, enabling robots to reason about indoor fungal contamination from weak, diffusion-dominated chemical signals remains an open challenge. We introduce Scensory, a learning-based robotic olfaction framework that simultaneously identifies fungal species and...","url_abs":"https://arxiv.org/abs/2509.19318","url_pdf":"https://arxiv.org/pdf/2509.19318v3","authors":"[\"Yanbaihui Liu\",\"Erica Babusci\",\"Claudia K. Gunsch\",\"Boyuan Chen\"]","published":"2025-09-11T21:13:32Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}