2026-09-20T18:10:59Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1634502025-10-30T03:21:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Kim, Sangbae Chignoli, Matthew T. Massachusetts Institute of Technology. Department of Mechanical Engineering 2025-10-29T17:42:13Z 2025-10-29T17:42:13Z 2025-05 2025-06-26T14:11:29.292Z https://hdl.handle.net/1721.1/163450 0000-0003-3066-7001 Legged robots have long been envisioned as a means of expanding robotic capabilities beyond structured environments, yet achieving high-agility locomotion remains a fundamental challenge. This thesis presents a model-based framework for parkour-style locomotion, enabling robots to execute highly dynamic maneuvers such as jumps, rolls, and flips with precision and robustness. A key challenge in planning these motions is selecting an appropriate dynamic model that balances computational efficiency with physical accuracy. To address this, a model assessment strategy is introduced to determine the simplest model capable of capturing task-relevant dynamics. Even with well-chosen models, solving long-horizon trajectory optimization problems for dynamic motions is computationally demanding. This thesis introduces graduated optimization techniques, which improve solver efficiency and reliability by generating high-quality initial guesses through progressively refined problem formulations. Additionally, a novel formulation of rigid-body dynamics algorithms for systems with kinematic loops accelerates trajectory optimization and simulation. Finally, two control strategies are proposed to execute planned motions on hardware: a model-based tracking controller for real-time adjustments and an imitation learning policy trained on optimal trajectories to enhance robustness. Extensive experiments on hardware validate the framework, demonstrating the successful execution of complex, high-impact locomotion behaviors. By integrating advanced planning, optimization, and control techniques, this work establishes a foundation for high-agility legged locomotion, pushing beyond conventional automation toward real-world, dynamic robotic movement. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="bf1bbec4-991b-464c-a321-7fa1cc1e512a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Chignoli, Matthew T.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Legged robots have long been envisioned as a means of expanding robotic capabilities beyond structured environments, yet achieving high-agility locomotion remains a fundamental challenge. This thesis presents a model-based framework for parkour-style locomotion, enabling robots to execute highly dynamic maneuvers such as jumps, rolls, and flips with precision and robustness. A key challenge in planning these motions is selecting an appropriate dynamic model that balances computational efficiency with physical accuracy. To address this, a model assessment strategy is introduced to determine the simplest model capable of capturing task-relevant dynamics. Even with well-chosen models, solving long-horizon trajectory optimization problems for dynamic motions is computationally demanding. This thesis introduces graduated optimization techniques, which improve solver efficiency and reliability by generating high-quality initial guesses through progressively refined problem formulations. Additionally, a novel formulation of rigid-body dynamics algorithms for systems with kinematic loops accelerates trajectory optimization and simulation. Finally, two control strategies are proposed to execute planned motions on hardware: a model-based tracking controller for real-time adjustments and an imitation learning policy trained on optimal trajectories to enhance robustness. Extensive experiments on hardware validate the framework, demonstrating the successful execution of complex, high-impact locomotion behaviors. By integrating advanced planning, optimization, and control techniques, this work establishes a foundation for high-agility legged locomotion, pushing beyond conventional automation toward real-world, dynamic robotic movement.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>