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Article: Optimizing walking controllers for uncertain inputs and environments

TitleOptimizing walking controllers for uncertain inputs and environments
Authors
Issue Date2010
Citation
ACM Transactions on Graphics, 2010, v. 29 n. 4 How to Cite?
AbstractWe introduce methods for optimizing physics-based walking controllers for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. These variables are represented with probability distributions, and a return function scores the desirability of a single motion. Controller optimization entails maximizing the expected value of the return, which is computed by Monte Carlo methods. We demonstrate examples with different sources of uncertainty and task constraints. Optimizing control strategies under uncertainty increases robustness and produces natural variations in style. © 2010 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/192719
ISSN
2015 Impact Factor: 4.218
2015 SCImago Journal Rankings: 2.552
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, JMen_US
dc.contributor.authorFleet, DJen_US
dc.contributor.authorHertzmann, Aen_US
dc.date.accessioned2013-11-20T04:59:01Z-
dc.date.available2013-11-20T04:59:01Z-
dc.date.issued2010en_US
dc.identifier.citationACM Transactions on Graphics, 2010, v. 29 n. 4en_US
dc.identifier.issn0730-0301en_US
dc.identifier.urihttp://hdl.handle.net/10722/192719-
dc.description.abstractWe introduce methods for optimizing physics-based walking controllers for robustness to uncertainty. Many unknown factors, such as external forces, control torques, and user control inputs, cannot be known in advance and must be treated as uncertain. These variables are represented with probability distributions, and a return function scores the desirability of a single motion. Controller optimization entails maximizing the expected value of the return, which is computed by Monte Carlo methods. We demonstrate examples with different sources of uncertainty and task constraints. Optimizing control strategies under uncertainty increases robustness and produces natural variations in style. © 2010 ACM.en_US
dc.languageengen_US
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.titleOptimizing walking controllers for uncertain inputs and environmentsen_US
dc.typeArticleen_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1145/1778765.1778810en_US
dc.identifier.scopuseid_2-s2.0-77956384479en_US
dc.identifier.volume29en_US
dc.identifier.issue4en_US
dc.identifier.isiWOS:000279806600043-

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