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oai:dspace.mit.edu:1721.1/1634292025-10-30T03:21:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Kim, Sangbae Khazoom, Charles Massachusetts Institute of Technology. Department of Mechanical Engineering 2025-10-29T17:40:39Z 2025-10-29T17:40:39Z 2025-05 2025-06-26T14:11:53.004Z https://hdl.handle.net/1721.1/163429 Humanoid robots promise human-like mobility, but must manage complex and often conflicting control objectives. While model-based controllers can address these challenges using online optimization, they have high computational demands. Model predictive control (MPC) provides closed-loop stability with online trajectory optimization, but achieving real-time rates is difficult for high-dimensional systems. To mitigate this limitation, most MPC implementations rely on reduced-order models (ROMs) that simplify planning but fail to capture whole-body constraints like joint limits and self-collisions. Reactive whole-body controllers (WBCs) partially address this limitation by projecting ROM trajectories onto some wholebody constraints, but these are restricted to acceleration-level constraints like friction cones and torque limits. This thesis advances humanoid planning and control through a renewed focus on model fidelity, solution accuracy ans solve times with three key contributions. First, we propose the CBF-WBC, which augments reactive WBCs with position constraints using control barrier functions (CBFs), enabling the MIT Humanoid to avoid selfcollisions with minimal computational overhead. As a result, the robot can reactively deviate from infeasible trajectories from a reduced-order MPC. Despite fast solve times below 100 microseconds, conflicts can arise between the reduced-order MPC and the CBF-WBC. To address this, we enable real-time whole-body MPC using the alternating direction method of multipliers (ADMM) to provide low-accuracy solutions at high feedback rates. The controller is reliably deployed on hardware and enables the MIT Humanoid to walk robustly on rough terrains and plan complex crossed-leg and arm motions that enhance stability when recovering from significant disturbances. While low-accuracy solutions often suffice for real-time control, we found that higher accuracy could still improve closed-loop performance if computational speed allows. Building on this insight, we propose a framework to simultaneously optimize solution accuracy and model complexity to maximize closed-loop performance. Instead of planning with a single model that is too complex or too simple, solve times can be reduced by planning over a sequence of models of reducing complexity. We extract ROMs from whole-body dynamics equations and optimize their horizons, discretization timesteps and solution accuracy using blackbox optimization. The optimizer can sacrifice model complexity for additional ADMM iterations, reducing falls by nine-fold and enabling a 2 m/s walking speed on hardware. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Tailoring Complexity of Model-Based Controllers for Legged Robots Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a844b030-90d3-4fe1-890b-d082496e2576"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Tailoring Complexity of Model-Based Controllers for Legged Robots</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Khazoom, Charles</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>Humanoid robots promise human-like mobility, but must manage complex and often conflicting control objectives. While model-based controllers can address these challenges using online optimization, they have high computational demands. Model predictive control (MPC) provides closed-loop stability with online trajectory optimization, but achieving real-time rates is difficult for high-dimensional systems. To mitigate this limitation, most MPC implementations rely on reduced-order models (ROMs) that simplify planning but fail to capture whole-body constraints like joint limits and self-collisions. Reactive whole-body controllers (WBCs) partially address this limitation by projecting ROM trajectories onto some wholebody constraints, but these are restricted to acceleration-level constraints like friction cones and torque limits. This thesis advances humanoid planning and control through a renewed focus on model fidelity, solution accuracy ans solve times with three key contributions. First, we propose the CBF-WBC, which augments reactive WBCs with position constraints using control barrier functions (CBFs), enabling the MIT Humanoid to avoid selfcollisions with minimal computational overhead. As a result, the robot can reactively deviate from infeasible trajectories from a reduced-order MPC. Despite fast solve times below 100 microseconds, conflicts can arise between the reduced-order MPC and the CBF-WBC. To address this, we enable real-time whole-body MPC using the alternating direction method of multipliers (ADMM) to provide low-accuracy solutions at high feedback rates. The controller is reliably deployed on hardware and enables the MIT Humanoid to walk robustly on rough terrains and plan complex crossed-leg and arm motions that enhance stability when recovering from significant disturbances. While low-accuracy solutions often suffice for real-time control, we found that higher accuracy could still improve closed-loop performance if computational speed allows. Building on this insight, we propose a framework to simultaneously optimize solution accuracy and model complexity to maximize closed-loop performance. Instead of planning with a single model that is too complex or too simple, solve times can be reduced by planning over a sequence of models of reducing complexity. We extract ROMs from whole-body dynamics equations and optimize their horizons, discretization timesteps and solution accuracy using blackbox optimization. The optimizer can sacrifice model complexity for additional ADMM iterations, reducing falls by nine-fold and enabling a 2 m/s walking speed on hardware.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>