{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120584"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120584","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing AutoMPC for efficient offline optimal control of robots through state constraints, improved system identification models and trajectory tracking controllers","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Jeong, Dohun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hauser, Kris K"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Robotics","Machine Learning","Control"],"languages":["en","eng"],"rights":["Copyright 2023 Dohun Jeong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120584","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris K"]},{"key":"dc:creator","label":"Author","values":["Jeong, Dohun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-03"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Robotics","Machine Learning","Control"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Dohun Jeong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120584"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Dohun Jeong, accepted the attached license on 2023-05-03 at 00:41.","The student, Dohun Jeong, submitted this Thesis for approval on 2023-05-03 at 00:45.","This Thesis was approved for publication on 2023-05-03 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19308 on 2023-09-01 at 17:22:18","Bringing up a robot can be a long, expensive, and dangerous process. This process becomes even more challenging when we build a controller using ma- chine learning techniques. Challenges include: 1) safely collecting a large and diverse enough dataset to the scale that expressive functional approxi- mators such as deep neural networks can capture the complex and nonlinear dynamics of the robot while trading off accuracy with computational load, 2) designing a controller that can reason about both short term constraints and long-term behavior to create an optimal strategy, 3) tuning the parameters of the policy to exhibit desirable behavior when deployed on a physical robot. While areas like reinforcement learning and optimal control address some of these problems, AutoMPC can address all of the above and create a con- troller without any physical interaction beyond the collection of a dataset. In this thesis, several additional features are introduced to make the AutoMPC library more applicable to a variety of robot domains. These include 1) the ability to bound observations in iLQR to ensure safe operation of the robot and prevent hallucination in simulations, 2) a reference trajectory tracking controller, and 3) a modified class of system identification model derived from multilayer perceptrons that uses history of states to predict the robot’s next state. These features are tested on a variety of tasks, including OpenAI gym tasks such as HalfCheetah and CartPole system, along with physical robot tasks on an underwater soft robot arm. This is the first successful application of AutoMPC pipeline to a physical robot, and it outperforms re- cent learning-based methods for creating an optimal controller offline. These features will be included in the public release of the 0.2 version release of AutoMPC, to bring an efficient and scalable solution to data-driven control to the wider research community."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing AutoMPC for efficient offline optimal control of robots through state constraints, improved system identification models and trajectory tracking controllers"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris K"],"dc:creator":["Jeong, Dohun"],"dc:date":["2023-05","2023-05-03"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Dohun Jeong, accepted the attached license on 2023-05-03 at 00:41.","The student, Dohun Jeong, submitted this Thesis for approval on 2023-05-03 at 00:45.","This Thesis was approved for publication on 2023-05-03 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19308 on 2023-09-01 at 17:22:18","Bringing up a robot can be a long, expensive, and dangerous process. This process becomes even more challenging when we build a controller using ma- chine learning techniques. Challenges include: 1) safely collecting a large and diverse enough dataset to the scale that expressive functional approxi- mators such as deep neural networks can capture the complex and nonlinear dynamics of the robot while trading off accuracy with computational load, 2) designing a controller that can reason about both short term constraints and long-term behavior to create an optimal strategy, 3) tuning the parameters of the policy to exhibit desirable behavior when deployed on a physical robot. While areas like reinforcement learning and optimal control address some of these problems, AutoMPC can address all of the above and create a con- troller without any physical interaction beyond the collection of a dataset. In this thesis, several additional features are introduced to make the AutoMPC library more applicable to a variety of robot domains. These include 1) the ability to bound observations in iLQR to ensure safe operation of the robot and prevent hallucination in simulations, 2) a reference trajectory tracking controller, and 3) a modified class of system identification model derived from multilayer perceptrons that uses history of states to predict the robot’s next state. These features are tested on a variety of tasks, including OpenAI gym tasks such as HalfCheetah and CartPole system, along with physical robot tasks on an underwater soft robot arm. This is the first successful application of AutoMPC pipeline to a physical robot, and it outperforms re- cent learning-based methods for creating an optimal controller offline. These features will be included in the public release of the 0.2 version release of AutoMPC, to bring an efficient and scalable solution to data-driven control to the wider research community."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120584"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Dohun Jeong"],"dc:subject":["Robotics","Machine Learning","Control"],"dc:title":["Enhancing AutoMPC for efficient offline optimal control of robots through state constraints, improved system identification models and trajectory tracking controllers"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}