{"paper":{"title":"Scensory: Real-Time Robotic Olfactory Perception for Joint Identification and Source Localization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Scensory enables robots to identify fungal species and localize sources from short VOC sensor readings using neural networks.","cross_cats":["cs.RO"],"primary_cat":"eess.SP","authors_text":"Boyuan Chen, Claudia K. Gunsch, Erica Babusci, Yanbaihui Liu","submitted_at":"2025-09-11T21:13:32Z","abstract_excerpt":"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."},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Temporal VOC dynamics encode both chemical and spatial signatures, which we decode through neural networks trained on robot-automated data collection with spatial supervision.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Scensory uses neural networks trained on robot-collected VOC time series to jointly identify fungal species and localize sources, reporting up to 89.85% species accuracy and 87.31% localization accuracy from short inputs under ambient conditions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Scensory enables robots to identify fungal species and localize sources from short VOC sensor readings using neural networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"cd897c3f8f93645ee874bad625feb7d1e9de667094812ef44d714521cc8ad249"},"source":{"id":"2509.19318","kind":"arxiv","version":3},"verdict":{"id":"76335f33-edb7-48e6-84c7-4a006b73a560","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T16:54:53.710561Z","strongest_claim":"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.","one_line_summary":"Scensory uses neural networks trained on robot-collected VOC time series to jointly identify fungal species and localize sources, reporting up to 89.85% species accuracy and 87.31% localization accuracy from short inputs under ambient conditions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Temporal VOC dynamics encode both chemical and spatial signatures, which we decode through neural networks trained on robot-automated data collection with spatial supervision.","pith_extraction_headline":"Scensory enables robots to identify fungal species and localize sources from short VOC sensor readings using neural networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.19318/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"08a83f233cf5b6c9bd257af79ea2a3f3a93e3331c884a265235e52ed0970f1dc"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}