{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125618"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125618","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards failure aware autonomy: robust control, anomaly detection, and robot assistance","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","abstract_has_math":false,"creators":["Ji, Tianchen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Driggs-Campbell, Katie","Chowdhary, Girish","Mitra, Sayan","Wang, Shenlong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-11","date_published":"2024-07-11","updated_at":"2026-07-22T22:25:02Z","subjects":["Robotics","Anomaly Detection","Machine Learning","Autonomous Systems"],"languages":["en","eng"],"rights":["Copyright 2024 Tianchen Ji"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125618","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Driggs-Campbell, Katie","Chowdhary, Girish","Mitra, Sayan","Wang, Shenlong"]},{"key":"dc:creator","label":"Author","values":["Ji, Tianchen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-11","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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","Anomaly Detection","Machine Learning","Autonomous Systems"]}]},{"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 2024 Tianchen Ji"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125618"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Tianchen Ji, accepted the attached license on 2024-07-11 at 15:09.","The student, Tianchen Ji, submitted this Dissertation for approval on 2024-07-11 at 15:28.","This Dissertation was approved for publication on 2024-07-11 at 16:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21075 on 2025-02-04 at 21:05:02","Robots are entering the open world with an ultimate goal of achieving full autonomy with no human intervention. However, due to complex and uncertain environments, it is hard and nearly impossible to claim that robots will be failure free under any real-world circumstances in the foreseeable future. To make matters worse, such vulnerability grows as robots get equipped with machine learning algorithms, which are brittle in unseen scenarios. Can we make robots reliable and trustworthy even with the presence of possible robot failures? In this dissertation, we try to answer this question by developing failure-aware autonomy that enables robots to prevent, detect, and recover from failures and/or anomalies in real-world applications. As long as failures are acknowledged, identified, and handled appropriately upon occurrence, robots can still deliver robust performance in complex and uncertain environments. To this end, we present approaches for (a) failure prevention via robust control, (b) learning-based anomaly detection in uncertain and interactive environments, and (c) failure recovery through robot assistance from a human supervisor. The key technical developments within these themes include: (1) A novel robust output feedback model predictive controller that guarantees constraint satisfaction and stability under ellipsoidal uncertainty. (2) A state-of-the-art anomaly detector for field robot navigation that can fuse heterogeneous high-dimensional sensor modalities effectively for robust perception and can alert robots proactively before navigation failures occur. (3) A novel unsupervised anomaly detector for autonomous driving that generates a comprehensive anomaly score through an ensemble of neural networks by learning multi-modal normal patterns in driving scenarios. (4) The first algorithm for solving a multi-robot assistance problem as a dynamic graph traversal problem with real-time performance on robot fleets of moderate size. The techniques jointly form failure-aware autonomy and achieve robust performance in real-world applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards failure aware autonomy: robust control, anomaly detection, and robot assistance"]}]}],"canonical_facts":{"dc:contributor":["Driggs-Campbell, Katie","Chowdhary, Girish","Mitra, Sayan","Wang, Shenlong"],"dc:creator":["Ji, Tianchen"],"dc:date":["2024-07-11","2024-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms","The student, Tianchen Ji, accepted the attached license on 2024-07-11 at 15:09.","The student, Tianchen Ji, submitted this Dissertation for approval on 2024-07-11 at 15:28.","This Dissertation was approved for publication on 2024-07-11 at 16:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21075 on 2025-02-04 at 21:05:02","Robots are entering the open world with an ultimate goal of achieving full autonomy with no human intervention. However, due to complex and uncertain environments, it is hard and nearly impossible to claim that robots will be failure free under any real-world circumstances in the foreseeable future. To make matters worse, such vulnerability grows as robots get equipped with machine learning algorithms, which are brittle in unseen scenarios. Can we make robots reliable and trustworthy even with the presence of possible robot failures? In this dissertation, we try to answer this question by developing failure-aware autonomy that enables robots to prevent, detect, and recover from failures and/or anomalies in real-world applications. As long as failures are acknowledged, identified, and handled appropriately upon occurrence, robots can still deliver robust performance in complex and uncertain environments. To this end, we present approaches for (a) failure prevention via robust control, (b) learning-based anomaly detection in uncertain and interactive environments, and (c) failure recovery through robot assistance from a human supervisor. The key technical developments within these themes include: (1) A novel robust output feedback model predictive controller that guarantees constraint satisfaction and stability under ellipsoidal uncertainty. (2) A state-of-the-art anomaly detector for field robot navigation that can fuse heterogeneous high-dimensional sensor modalities effectively for robust perception and can alert robots proactively before navigation failures occur. (3) A novel unsupervised anomaly detector for autonomous driving that generates a comprehensive anomaly score through an ensemble of neural networks by learning multi-modal normal patterns in driving scenarios. (4) The first algorithm for solving a multi-robot assistance problem as a dynamic graph traversal problem with real-time performance on robot fleets of moderate size. 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