{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/43209"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/43209","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Body and brain quality-diversity in robot swarms","abstract":"Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment.","abstract_html":"Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment.","abstract_has_math":false,"creators":["Mkhatshwa, Sindiso"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nitschke, Geoff"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T01:33:51Z","subjects":["Quality Diversity","QD algorithm"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/43209","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nitschke, Geoff"]},{"key":"dc:creator","label":"Author","values":["Mkhatshwa, Sindiso"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-05-08T12:11:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-08T12:11:19Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Quality Diversity","QD algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/43209"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment."]},{"key":"dc:title","label":"Title","values":["Body and brain quality-diversity in robot swarms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nitschke, Geoff"],"dc:creator":["Mkhatshwa, Sindiso"],"dc:date.accessioned":["2026-05-08T12:11:19Z"],"dc:date.available":["2026-05-08T12:11:19Z"],"dc:date.issued":["2023"],"dc:description.abstract":["Various studies have shown that diverse groups perform better, solve problems more adeptly, and are more resilient. However, in evolutionary robotics, evolving group diversity is a difficult task that frequently calls for geographic isolation, a division of labor mechanism, and a careful choice of parameters. According to recent research, decentralized Quality Diversity (QD) algorithms can generate behavioral diversity across a swarm without requiring geographical isolation or a division of labor mechanism. Despite the fact that these findings represent an essential first step in the quest to find a mechanism to evolve behavioral diversity across a swarm in physical robot tasks, little research has been done on evolving behavior-morphology diversity across a robot swarm given cooperative tasks. To address this issue, we investigate the application of a decentralized QD algorithm (EDQD) to generate group diversity given an increasingly challenging collective behavior task in order to determine the circumstances in which it succeeds and fails. We further develop Double-Map EDQD-M, an algorithm that combines morphology characterization and behavior characterization (body-brain diversity maintenance). Results indicate that body-brain diversity maintenance yielded significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment."],"dc:identifier.uri":["http://hdl.handle.net/11427/43209"],"dc:language.iso":["en"],"dc:publisher.department":["Department of Computer Science"],"dc:publisher.institution":["University of Cape Town"],"dc:subject":["Quality Diversity","QD algorithm"],"dc:title":["Body and brain quality-diversity in robot swarms"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters"]},"updated_at":"2026-07-24T01:33:51Z"}