{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classification-of-household-materials-via","title":"Classification of Household Materials via Spectroscopy","arxiv_id":"1805.04051","date":"2018-05-10","proceeding":null,"authors":["Zackory Erickson","Nathan Luskey","Sonia Chernova","Charles C. Kemp"],"abstract":"Recognizing an object's material can inform a robot on the object's fragility\nor appropriate use. To estimate an object's material during manipulation, many\nprior works have explored the use of haptic sensing. In this paper, we explore\na technique for robots to estimate the materials of objects using spectroscopy.\nWe demonstrate that spectrometers provide several benefits for material\nrecognition, including fast response times and accurate measurements with low\nnoise. Furthermore, spectrometers do not require direct contact with an object.\nTo explore this, we collected a dataset of spectral measurements from two\ncommercially available spectrometers during which a robotic platform interacted\nwith 50 flat material objects, and we show that a neural network model can\naccurately analyze these measurements. Due to the similarity between\nconsecutive spectral measurements, our model achieved a material classification\naccuracy of 94.6% when given only one spectral sample per object. Similar to\nprior works with haptic sensors, we found that generalizing material\nrecognition to new objects posed a greater challenge, for which we achieved an\naccuracy of 79.1% via leave-one-object-out cross-validation. Finally, we\ndemonstrate how a PR2 robot can leverage spectrometers to estimate the\nmaterials of everyday objects found in the home. From this work, we find that\nspectroscopy poses a promising approach for material classification during\nrobotic manipulation.","url_abs":"http://arxiv.org/abs/1805.04051v3","url_pdf":"http://arxiv.org/pdf/1805.04051v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classification-of-household-materials-via","repo_url":"https://github.com/Healthcare-Robotics/smm50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"classification-of-household-materials-via","repo_url":"https://github.com/kebasaa/SCIO-read","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"material-classification","task_name":"Material Classification"},{"task_slug":"material-recognition","task_name":"Material Recognition"},{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}