2026-09-19T20:01:46Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117799 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

West, Matthew West, Matthew Park, Hae-Won Bretl, Timothy Ramos, Joao Righetti, Ludovic 2022-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms The student, Chuanzheng Li, accepted the attached license on 2022-11-29 at 12:45. The student, Chuanzheng Li, submitted this Dissertation for approval on 2022-11-29 at 13:41. This Dissertation was approved for publication on 2022-11-30 at 13:01. DSpace SAF Submission Ingestion Package generated from Vireo submission #18671 on 2023-04-12 at 07:35:41 Dynamic legged locomotion through trajectory optimization and reinforcement learning Li, Chuanzheng 2022-11-30 Robotics Trajectory Optimization Reinforcement Learning Agile quadrupeds such as cats and squirrels are capable of planning and per- forming highly dynamic maneuvers that fully utilize their physical capabilities and respect their inherent dynamics. The major challenges in endowing legged robots with such abilities arise from the requirement to generate feasible motions for such high-degree-of-motion systems under tight time constraints, and to reliably and reactively handle the constant making and breaking of contacts with their environment. This dissertation presents a three-part effort in addressing these challenges. First, I introduce the mechatronics design of multiple customized legged hardware platforms for validating the theoretical work that follows. Second, I propose a trajectory optimization framework for planning dynamic locomotion based on a robot’s centroidal momentum (CM), which is the sum of all the links’ momenta at its center of mass (CoM). By parameterizing the ground reaction force (GRF) and swing leg trajectories with Bezier polynomials, this framework can utilize the simple CM dynamics to produce feasible GRF and joint trajectories simultaneously under kinematic and dynamic constraints, while suffering less error caused by numerical integration. Third, I present the application of reinforcement learning (RL) in producing a jumping controller that achieves zero-shot sim-to-real transfer. At its core is a high-fidelity simulation environment enabled by identification of critical modeling details including contact compliance and an improved motor saturation model. Combined with a hierarchical control structure and a two-phase learning curriculum, this RL framework can generate controllers that consistently break the previous height record and produce experiment results that closely match those from simulation. Experimental validation of each controller design is performed on their corresponding customized hardware, proving their effectiveness in realizing dynamic motions on legged robots. Thesis eng https://hdl.handle.net/2142/117799 Copyright 2022 Chuanzheng Li Ph.D. Dissertation Mechanical Engineering University of Illinois at Urbana-Champaign Mechanical Sci & Engineering