{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T10:03:41Z","timestamp":1777716221088,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2012,4,1]],"date-time":"2012-04-01T00:00:00Z","timestamp":1333238400000},"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":[[2012,4]]},"abstract":"<jats:p>We describe a vision-based algorithm that enables a robot to robustly detect specific objects in a scene following an initial segmentation hint from a human user. The novelty lies in the ability to \u2018reacquire\u2019 objects over extended spatial and temporal excursions within challenging environments based upon a single training example. The primary difficulty lies in achieving an effective reacquisition capability that is robust to the effects of local clutter, lighting variation, and object relocation. We overcome these challenges through an adaptive detection algorithm that automatically generates multiple-view appearance models for each object online. As the robot navigates within the environment and the object is detected from different viewpoints, the one-shot learner opportunistically and automatically incorporates additional observations into each model. In order to overcome the effects of \u2018drift\u2019 common to adaptive learners, the algorithm imposes simple requirements on the geometric consistency of candidate observations. Motivating our reacquisition strategy is our work developing a mobile manipulator that interprets and autonomously performs commands conveyed by a human user. The ability to detect specific objects and reconstitute the user\u2019s segmentation hints enables the robot to be situationally aware. This situational awareness enables rich command and control mechanisms and affords natural interaction. We demonstrate one such capability that allows the human to give the robot a \u2018guided tour\u2019 of named objects within an outdoor environment and, hours later, to direct the robot to manipulate those objects by name using spoken instructions. We implemented our appearance-based detection strategy on our robotic manipulator as it operated over multiple days in different outdoor environments. We evaluate the algorithm\u2019s performance under challenging conditions that include scene clutter, lighting and viewpoint variation, object ambiguity, and object relocation. The results demonstrate a reacquisition capability that is effective in real-world settings.<\/jats:p>","DOI":"10.1177\/0278364911435515","type":"journal-article","created":{"date-parts":[[2012,4,4]],"date-time":"2012-04-04T12:12:55Z","timestamp":1333541575000},"page":"554-567","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["One-shot visual appearance learning for mobile manipulation"],"prefix":"10.1177","volume":"31","author":[{"given":"Matthew R","family":"Walter","sequence":"first","affiliation":[{"name":"MIT CS & AI Lab (CSAIL), Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuli","family":"Friedman","sequence":"additional","affiliation":[{"name":"BAE Systems, Burlington, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Antone","sequence":"additional","affiliation":[{"name":"BAE Systems, Burlington, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seth","family":"Teller","sequence":"additional","affiliation":[{"name":"MIT CS & AI Lab (CSAIL), Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2012,4,4]]},"reference":[{"key":"bibr1-0278364911435515","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206737"},{"key":"bibr2-0278364911435515","doi-asserted-by":"publisher","DOI":"10.1142\/S0219843604000150"},{"key":"bibr3-0278364911435515","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2009.5152739"},{"key":"bibr4-0278364911435515","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.205"},{"key":"bibr5-0278364911435515","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1195991"},{"key":"bibr6-0278364911435515","first-page":"253","volume-title":"Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction (HRI)","author":"Correa 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