{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/163429"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/163429","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Tailoring Complexity of Model-Based Controllers for Legged Robots","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_html":"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_has_math":false,"creators":["Khazoom, Charles"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Tailoring Complexity of Model-Based Controllers for Legged Robots"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kim, Sangbae"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Khazoom, Charles"],"dc:date.accessioned":["2025-10-29T17:40:39Z"],"dc:date.available":["2025-10-29T17:40:39Z"],"dc:date.issued":["2025-05"],"dc:description.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."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/163429"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Tailoring Complexity of Model-Based Controllers for Legged Robots"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:55Z"}