{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/43591"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/43591","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Adaptation of task-aware, communicative variance for motion control in social humanoid robotic applications","abstract":"An algorithm for generating communicative, human-like motion for social humanoid robots was developed. Anticipation, exaggeration, and secondary motion were demonstrated as examples of communication. Spatiotemporal correspondence was presented as a metric for human-like motion, and the metric was used to both synthesize and evaluate motion. An algorithm for generating an infinite number of variants from a single exemplar was established to avoid repetitive motion. The algorithm was made task-aware by including the functionality of satisfying constraints. User studies were performed with the algorithm using human participants. Results showed that communicative, human-like motion can be harnessed to direct partner attention and communicate state information. Furthermore, communicative, human-like motion for social robots produced by the algorithm allows humans partners to feel more engaged in the interaction, recognize motion earlier, label intent sooner, and remember interaction details more accurately.","abstract_html":"An algorithm for generating communicative, human-like motion for social humanoid robots was developed. Anticipation, exaggeration, and secondary motion were demonstrated as examples of communication. Spatiotemporal correspondence was presented as a metric for human-like motion, and the metric was used to both synthesize and evaluate motion. An algorithm for generating an infinite number of variants from a single exemplar was established to avoid repetitive motion. The algorithm was made task-aware by including the functionality of satisfying constraints. User studies were performed with the algorithm using human participants. Results showed that communicative, human-like motion can be harnessed to direct partner attention and communicate state information. Furthermore, communicative, human-like motion for social robots produced by the algorithm allows humans partners to feel more engaged in the interaction, recognize motion earlier, label intent sooner, and remember interaction details more accurately.","abstract_has_math":false,"creators":["Gielniak, Michael Joseph"],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":["Howard, Ayanna M.","Thomaz, Andrea L."],"committee_chairs":[],"committee_members":["Habetler, Tom","Liu, C. Karen","Ting, Lena","Vela, Patricio"],"year":2012,"date_issued":"2012-01-17","date_published":"2012-01-17","updated_at":"2026-07-27T19:50:58Z","subjects":["Motor coordination","DOF coupling","Kolmogorov-Sinai entropy","Animation principles"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1853/43591","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Howard, Ayanna M.","Thomaz, Andrea L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Habetler, Tom","Liu, C. Karen","Ting, Lena","Vela, Patricio"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Gielniak, Michael Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-06-06T16:42:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-06-06T16:42:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-01-17"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Motor coordination","DOF coupling","Kolmogorov-Sinai entropy","Animation principles"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1853/43591"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["An algorithm for generating communicative, human-like motion for social humanoid robots was developed. Anticipation, exaggeration, and secondary motion were demonstrated as examples of communication. Spatiotemporal correspondence was presented as a metric for human-like motion, and the metric was used to both synthesize and evaluate motion. An algorithm for generating an infinite number of variants from a single exemplar was established to avoid repetitive motion. The algorithm was made task-aware by including the functionality of satisfying constraints. User studies were performed with the algorithm using human participants. Results showed that communicative, human-like motion can be harnessed to direct partner attention and communicate state information. Furthermore, communicative, human-like motion for social robots produced by the algorithm allows humans partners to feel more engaged in the interaction, recognize motion earlier, label intent sooner, and remember interaction details more accurately."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["PhD"]},{"key":"dc:title","label":"Title","values":["Adaptation of task-aware, communicative variance for motion control in social humanoid robotic applications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Howard, Ayanna M.","Thomaz, Andrea L."],"dc:contributor.committeemember":["Habetler, Tom","Liu, C. Karen","Ting, Lena","Vela, Patricio"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Gielniak, Michael Joseph"],"dc:date.accessioned":["2012-06-06T16:42:55Z"],"dc:date.available":["2012-06-06T16:42:55Z"],"dc:date.issued":["2012-01-17"],"dc:description.abstract":["An algorithm for generating communicative, human-like motion for social humanoid robots was developed. Anticipation, exaggeration, and secondary motion were demonstrated as examples of communication. Spatiotemporal correspondence was presented as a metric for human-like motion, and the metric was used to both synthesize and evaluate motion. An algorithm for generating an infinite number of variants from a single exemplar was established to avoid repetitive motion. The algorithm was made task-aware by including the functionality of satisfying constraints. User studies were performed with the algorithm using human participants. Results showed that communicative, human-like motion can be harnessed to direct partner attention and communicate state information. Furthermore, communicative, human-like motion for social robots produced by the algorithm allows humans partners to feel more engaged in the interaction, recognize motion earlier, label intent sooner, and remember interaction details more accurately."],"dc:description.degree":["PhD"],"dc:identifier.uri":["http://hdl.handle.net/1853/43591"],"dc:publisher":["Georgia Institute of Technology"],"dc:subject":["Motor coordination","DOF coupling","Kolmogorov-Sinai entropy","Animation principles"],"dc:title":["Adaptation of task-aware, communicative variance for motion control in social humanoid robotic applications"],"dc:type":["Text"]},"updated_at":"2026-07-27T19:50:58Z"}