{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/75155"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/75155","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Learning Heterogeneous Resource-Constrained Task Allocation Using Concurrent Multi-Task Bandits","abstract":"Task allocation is a critical aspect of multi-robot coordination, enabling the completion of complex tasks that would be intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user in advance. In this thesis, we explore the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To tackle this challenge, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions in such a way that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we propose an online optimization algorithm called Concurrent Multi-Task Adaptive Bandits (CMTAB), which leverages and builds upon continuum-armed bandit algorithms. Our experiments, which involve detailed numerical simulations and a simulated emergency response task, demonstrate that CMTAB is effective at balancing exploration and exploitation to efficiently optimize unknown task rewards while respecting the team's resource constraints. Our results suggest that CMTAB has the potential to enable effective task allocation for multi-robot teams in real-world scenarios, even when task reward functions are unknown.","abstract_html":"Task allocation is a critical aspect of multi-robot coordination, enabling the completion of complex tasks that would be intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user in advance. In this thesis, we explore the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To tackle this challenge, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions in such a way that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we propose an online optimization algorithm called Concurrent Multi-Task Adaptive Bandits (CMTAB), which leverages and builds upon continuum-armed bandit algorithms. Our experiments, which involve detailed numerical simulations and a simulated emergency response task, demonstrate that CMTAB is effective at balancing exploration and exploitation to efficiently optimize unknown task rewards while respecting the team's resource constraints. Our results suggest that CMTAB has the potential to enable effective task allocation for multi-robot teams in real-world scenarios, even when task reward functions are unknown.","abstract_has_math":false,"creators":["Singh, Sukriti"],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":"Masters","degree_discipline":null,"degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":["Ravichandar, Harish"],"committee_chairs":[],"committee_members":["Chernova, Sonia","Gombolay, Matthew","Hutchinson, Seth"],"year":2023,"date_issued":"2023-05-02","date_published":"2023-05-02","updated_at":"2026-07-27T19:50:47Z","subjects":["Task allocation","Coalition formation","Heterogeneous teams","multi-arm bandits","Active learning"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1853/75155","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ravichandar, Harish"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chernova, Sonia","Gombolay, Matthew","Hutchinson, Seth"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Singh, Sukriti"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-05-23T19:22:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-05-23T19:22:38Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-05-02"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Task allocation","Coalition formation","Heterogeneous teams","multi-arm bandits","Active learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1853/75155"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Task allocation is a critical aspect of multi-robot coordination, enabling the completion of complex tasks that would be intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user in advance. In this thesis, we explore the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To tackle this challenge, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions in such a way that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we propose an online optimization algorithm called Concurrent Multi-Task Adaptive Bandits (CMTAB), which leverages and builds upon continuum-armed bandit algorithms. Our experiments, which involve detailed numerical simulations and a simulated emergency response task, demonstrate that CMTAB is effective at balancing exploration and exploitation to efficiently optimize unknown task rewards while respecting the team's resource constraints. Our results suggest that CMTAB has the potential to enable effective task allocation for multi-robot teams in real-world scenarios, even when task reward functions are unknown."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.S."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning Heterogeneous Resource-Constrained Task Allocation Using Concurrent Multi-Task Bandits"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ravichandar, Harish"],"dc:contributor.committeemember":["Chernova, Sonia","Gombolay, Matthew","Hutchinson, Seth"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Singh, Sukriti"],"dc:date.accessioned":["2024-05-23T19:22:38Z"],"dc:date.available":["2024-05-23T19:22:38Z"],"dc:date.issued":["2023-05-02"],"dc:description.abstract":["Task allocation is a critical aspect of multi-robot coordination, enabling the completion of complex tasks that would be intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user in advance. In this thesis, we explore the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To tackle this challenge, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions in such a way that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we propose an online optimization algorithm called Concurrent Multi-Task Adaptive Bandits (CMTAB), which leverages and builds upon continuum-armed bandit algorithms. Our experiments, which involve detailed numerical simulations and a simulated emergency response task, demonstrate that CMTAB is effective at balancing exploration and exploitation to efficiently optimize unknown task rewards while respecting the team's resource constraints. Our results suggest that CMTAB has the potential to enable effective task allocation for multi-robot teams in real-world scenarios, even when task reward functions are unknown."],"dc:description.degree":["M.S."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1853/75155"],"dc:language.iso":["en_US"],"dc:publisher":["Georgia Institute of Technology"],"dc:subject":["Task allocation","Coalition formation","Heterogeneous teams","multi-arm bandits","Active learning"],"dc:title":["Learning Heterogeneous Resource-Constrained Task Allocation Using Concurrent Multi-Task Bandits"],"dc:type":["Text"],"thesis:degree_level":["Masters"]},"updated_at":"2026-07-27T19:50:47Z"}