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dc.contributor.authorErdogan, Can
dc.contributor.authorStilman, Mike
dc.date.accessioned2013-07-17T18:11:08Z
dc.date.available2013-07-17T18:11:08Z
dc.date.issued2013-05
dc.identifier.citationErdogan, C. & Stilman, M. (2013). "Planning in Constraint Space: Automated Design of Functional Structures". Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), 6-10 May 2013.en_US
dc.identifier.urihttp://hdl.handle.net/1853/48436
dc.description© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.descriptionPresented at the 2013 IEEE International Conference on Robotics and Automation (ICRA), 6-10 May 2013, Karlsruhe, Germany.
dc.description.abstractOn the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectAutonomous agentsen_US
dc.titlePlanning in Constraint Space: Automated Design of Functional Structuresen_US
dc.typePost-printen_US
dc.typeProceedings
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.embargo.termsnullen_US


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