{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:22:59Z","timestamp":1766136179421,"version":"3.38.0"},"reference-count":44,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2010,12,7]],"date-time":"2010-12-07T00:00:00Z","timestamp":1291680000000},"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":[[2011,5]]},"abstract":"<jats:p> Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC. <\/jats:p>","DOI":"10.1177\/0278364910386985","type":"journal-article","created":{"date-parts":[[2010,12,8]],"date-time":"2010-12-08T01:47:55Z","timestamp":1291772875000},"page":"678-698","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":98,"title":["Randomized multi-modal motion planning for a humanoid robot manipulation task"],"prefix":"10.1177","volume":"30","author":[{"given":"Kris","family":"Hauser","sequence":"first","affiliation":[{"name":"Indiana University, Bloomington, IN, USA,"}]},{"given":"Victor","family":"Ng-Thow-Hing","sequence":"additional","affiliation":[{"name":"Honda Research Institute, Mountain View, CA, USA"}]}],"member":"179","published-online":{"date-parts":[[2010,12,7]]},"reference":[{"key":"atypb1","unstructured":"Alami R., Laumond J-P and Sim\u00e9on T. 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