{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108550"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108550","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Model-based approaches for learning control from multi-modal data","abstract":"Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has been great difficulty in applying these algorithms to real-world systems due to poor sample efficiency and inability to handle state and control constraints. We introduce and demonstrate a general paradigm that combines model-learning and online planning for control which can also handle a wide range of problems using traditional and non-traditional sensor information. Rather than using popular RL methods, learning a model from data and performing online planning in the form of model predictive control (MPC) can be much more data-efficient and practical for deploying on real robotics systems. In addition to a generally applicable sample-based planning strategy, another specific formulation of model learning is investigated that allows for a linear structure to be exploited for efficient control. The algorithms are validated in both simulation and on real robotic platforms, namely an agriculture berry-picking robot using a soft-continuum arm. The model-based method is not only able to solve a challenging soft-body control task, but also can be deployed in a field setting where model-free RL is bottle-necked by data-efficiency.","abstract_html":"Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has been great difficulty in applying these algorithms to real-world systems due to poor sample efficiency and inability to handle state and control constraints. We introduce and demonstrate a general paradigm that combines model-learning and online planning for control which can also handle a wide range of problems using traditional and non-traditional sensor information. Rather than using popular RL methods, learning a model from data and performing online planning in the form of model predictive control (MPC) can be much more data-efficient and practical for deploying on real robotics systems. In addition to a generally applicable sample-based planning strategy, another specific formulation of model learning is investigated that allows for a linear structure to be exploited for efficient control. The algorithms are validated in both simulation and on real robotic platforms, namely an agriculture berry-picking robot using a soft-continuum arm. The model-based method is not only able to solve a challenging soft-body control task, but also can be deployed in a field setting where model-free RL is bottle-necked by data-efficiency.","abstract_has_math":false,"creators":["Havens, Aaron"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-07-24","date_published":"2020-07-24","updated_at":"2026-07-22T22:24:48Z","subjects":["model predictive control","reinforcement learning","soft robotics"],"languages":["en"],"rights":["Copyright 2020 Aaron Havens"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108550","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Havens, Aaron"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-07-24","2020-10-07T21:00:13Z","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["model predictive control","reinforcement learning","soft robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Aaron Havens"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108550"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has been great difficulty in applying these algorithms to real-world systems due to poor sample efficiency and inability to handle state and control constraints. We introduce and demonstrate a general paradigm that combines model-learning and online planning for control which can also handle a wide range of problems using traditional and non-traditional sensor information. Rather than using popular RL methods, learning a model from data and performing online planning in the form of model predictive control (MPC) can be much more data-efficient and practical for deploying on real robotics systems. In addition to a generally applicable sample-based planning strategy, another specific formulation of model learning is investigated that allows for a linear structure to be exploited for efficient control. The algorithms are validated in both simulation and on real robotic platforms, namely an agriculture berry-picking robot using a soft-continuum arm. The model-based method is not only able to solve a challenging soft-body control task, but also can be deployed in a field setting where model-free RL is bottle-necked by data-efficiency.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","The student, Aaron Havens, accepted the attached license on 2020-07-24 at 15:11.","The student, Aaron Havens, submitted this Thesis for approval on 2020-07-24 at 15:49.","This Thesis was approved for publication on 2020-07-24 at 16:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15754 on 2020-10-02 at 15:15:32","Made available in DSpace on 2020-10-07T21:00:13Z (GMT). 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We introduce and demonstrate a general paradigm that combines model-learning and online planning for control which can also handle a wide range of problems using traditional and non-traditional sensor information. Rather than using popular RL methods, learning a model from data and performing online planning in the form of model predictive control (MPC) can be much more data-efficient and practical for deploying on real robotics systems. In addition to a generally applicable sample-based planning strategy, another specific formulation of model learning is investigated that allows for a linear structure to be exploited for efficient control. The algorithms are validated in both simulation and on real robotic platforms, namely an agriculture berry-picking robot using a soft-continuum arm. 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