{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:51:46Z","timestamp":1777715506041,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2015,8,12]],"date-time":"2015-08-12T00:00:00Z","timestamp":1439337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2016,6]]},"abstract":"<jats:p>In robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision and recall have been widely adopted as canonical metrics to quantify the performance of learning algorithms, but for robotics applications involving mission-critical decision making, good performance in relation to these metrics is insufficient. We introduce and motivate the importance of a classifier\u2019s introspective capacity: the ability to associate an appropriate assessment of confidence with any test case. We propose that a key ingredient for introspection is a framework\u2019s potential to increase its uncertainty with the distance between a test datum its training data. We compare the introspective capacities of a number of commonly used classification frameworks in both classification and detection tasks, and show that better introspection leads to improved decision making in the context of tasks such as autonomous driving or semantic map generation.<\/jats:p>","DOI":"10.1177\/0278364915587924","type":"journal-article","created":{"date-parts":[[2015,8,12]],"date-time":"2015-08-12T20:48:34Z","timestamp":1439412514000},"page":"743-762","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":31,"title":["Introspective classification for robot perception"],"prefix":"10.1177","volume":"35","author":[{"given":"Hugo","family":"Grimmett","sequence":"first","affiliation":[{"name":"Mobile Robotics Group, Oxford University, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rudolph","family":"Triebel","sequence":"additional","affiliation":[{"name":"Technical University of M\u00fcnchen, M\u00fcnchen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohan","family":"Paul","sequence":"additional","affiliation":[{"name":"Mobile Robotics Group, Oxford University, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingmar","family":"Posner","sequence":"additional","affiliation":[{"name":"Mobile Robotics Group, Oxford University, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,8,12]]},"reference":[{"key":"bibr1-0278364915587924","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.133"},{"key":"bibr2-0278364915587924","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139637"},{"key":"bibr3-0278364915587924","unstructured":"Bishop CM (2006) Pattern Recognition and Machine Learning. 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