{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:17:57Z","timestamp":1778858277155,"version":"3.51.4"},"reference-count":93,"publisher":"SAGE Publications","issue":"14","license":[{"start":{"date-parts":[[2013,10,4]],"date-time":"2013-10-04T00:00:00Z","timestamp":1380844800000},"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":[[2013,12]]},"abstract":"<jats:p>Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behavior in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behavior by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behavior that does not use them. We evaluate the T-resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to an RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 min, T-resilience consistently leads to substantially better results than the other approaches.<\/jats:p>","DOI":"10.1177\/0278364913499192","type":"journal-article","created":{"date-parts":[[2013,10,4]],"date-time":"2013-10-04T23:07:00Z","timestamp":1380928020000},"page":"1700-1723","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":60,"title":["Fast damage recovery in robotics with the T-resilience algorithm"],"prefix":"10.1177","volume":"32","author":[{"given":"Sylvain","family":"Koos","sequence":"first","affiliation":[{"name":"ISIR, Universit\u00e9 Pierre et Marie Curie, Paris, France"}]},{"given":"Antoine","family":"Cully","sequence":"additional","affiliation":[{"name":"ISIR, Universit\u00e9 Pierre et Marie Curie, Paris, France"}]},{"given":"Jean-Baptiste","family":"Mouret","sequence":"additional","affiliation":[{"name":"ISIR, Universit\u00e9 Pierre et Marie Curie, Paris, France"}]}],"member":"179","published-online":{"date-parts":[[2013,10,4]]},"reference":[{"key":"bibr1-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2008.10.024"},{"key":"bibr2-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2006.04.009"},{"key":"bibr3-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1126\/science.1146230"},{"key":"bibr4-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1109\/EH.2005.30"},{"key":"bibr5-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1145\/1276958.1277004"},{"key":"bibr6-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.850293"},{"key":"bibr7-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1126\/science.1133687"},{"key":"bibr8-0278364913499192","volume-title":"Fault Diagnosis and Fault Tolerance for Mechatronic Systems: Recent Advances","author":"Caccavale F","year":"2002"},{"key":"bibr9-0278364913499192","volume-title":"Efficient and Accurate Parallel Genetic Algorithms","author":"Cantu-Paz E","year":"2000"},{"key":"bibr10-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"bibr11-0278364913499192","first-page":"2562","volume-title":"Proceedings of IEEE\/RSJ international conference on intelligent robots and systems (IROS)","author":"Chernova S","year":"2004"},{"key":"bibr12-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2104157"},{"key":"bibr13-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3184-5"},{"issue":"9","key":"bibr14-0278364913499192","first-page":"72","volume":"34","author":"Corbato F","year":"2007","journal-title":"ACM Turing Award Lectures"},{"key":"bibr15-0278364913499192","doi-asserted-by":"publisher","DOI":"10.1145\/2463372.2463399"},{"key":"bibr16-0278364913499192","unstructured":"Cully A, Mouret JB (2013 b) Learning to walk in every direction. 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