{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147477"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147477","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Learning Large-scale Multi-agent Control with Safety Certificates","abstract":"Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability.","abstract_html":"Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability.","abstract_has_math":false,"creators":["Qin, Zengyi"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Aeronautics and Astronautics","school":null,"contributors":[],"advisors":["How, Jonathan P."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-09","date_published":"2022-09","updated_at":"2026-07-22T22:20:50Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/147477","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["How, Jonathan P."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Aeronautics and Astronautics"]},{"key":"dc:creator","label":"Author","values":["Qin, Zengyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-01-19T19:53:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-01-19T19:53:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Aeronautics and Astronautics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/147477"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Learning Large-scale Multi-agent Control with Safety Certificates"]}]}],"canonical_facts":{"dc:contributor.advisor":["How, Jonathan P."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Aeronautics and Astronautics"],"dc:creator":["Qin, Zengyi"],"dc:date.accessioned":["2023-01-19T19:53:02Z"],"dc:date.available":["2023-01-19T19:53:02Z"],"dc:date.issued":["2022-09"],"dc:description.abstract":["Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/147477"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Learning Large-scale Multi-agent Control with Safety Certificates"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Aeronautics and Astronautics"]},"updated_at":"2026-07-22T22:20:50Z"}