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Article: Optimizing walking controllers for uncertain inputs and environments
Title | Optimizing walking controllers for uncertain inputs and environments |
---|---|
Authors | |
Keywords | Controller synthesis Human motion Optimization Physics-based animation |
Issue Date | 2010 |
Citation | ACM Transactions on Graphics, 2010, v. 29 n. 4 How to Cite? |
Abstract | We 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 Identifier | http://hdl.handle.net/10722/192719 |
ISSN | 2021 Impact Factor: 7.403 2020 SCImago Journal Rankings: 2.153 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, JM | en_US |
dc.contributor.author | Fleet, DJ | en_US |
dc.contributor.author | Hertzmann, A | en_US |
dc.date.accessioned | 2013-11-20T04:59:01Z | - |
dc.date.available | 2013-11-20T04:59:01Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.citation | ACM Transactions on Graphics, 2010, v. 29 n. 4 | en_US |
dc.identifier.issn | 0730-0301 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/192719 | - |
dc.description.abstract | We 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.language | eng | en_US |
dc.relation.ispartof | ACM Transactions on Graphics | en_US |
dc.subject | Controller synthesis | - |
dc.subject | Human motion | - |
dc.subject | Optimization | - |
dc.subject | Physics-based animation | - |
dc.title | Optimizing walking controllers for uncertain inputs and environments | en_US |
dc.type | Article | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1145/1778765.1778810 | en_US |
dc.identifier.scopus | eid_2-s2.0-77956384479 | en_US |
dc.identifier.volume | 29 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.isi | WOS:000279806600043 | - |
dc.identifier.issnl | 0730-0301 | - |