Towards adaptative wheeled humanoid control: Leveraging fast parameter estimation and sim-to-real adaptation

Despite promising advancements in robotics, robots are still far from achieving humanlevel capabilities, particularly when it comes to interacting with their environments. In contrast, humans are surprisingly good at adapting to the environment. On of the key secrets behind this capability comes from that we are already familiar with our surrounding environment and could use our intuition based on this information. This raises the question: ”What if we could quickly provide environmental information to a robot?” Furthermore, how beneficial would it be to give this information to robots in a more explicit manner? With those questions in mind, in this work, we propose ‘fast physical parameter estimation’ and ‘model and learning-based control framework’ to explicitly utilize the estimated physical parameters. Specifically, we focus on building adaptive wheeled humanoid control targeting our customized wheeled humanoid robot, SATYRR. Our framework comprises three key components: 1) sim-to-real adaptation, 2) sampling or learning-based parameter estimation, and 3) model or learning-based control. Sim-to-real adaptation enables accurate data and sample collection without needing to operate the physical hardware for specific tasks. This also provides the capability of decoupling the robot dynamics and object dynamics. Sampling and learning-based estimators are typically faster than optimization methods, relying solely on the robot’s proprioception without requiring additional sensors. Finally, the model or learning-based controllers—whether reinforcement learning, MPC, or other optimization-based approaches—can explicitly integrate the estimated parameters into their control formulations. We demonstrated our framework in physical hardware and showed its benefit in enhancing the control performance while making the system more adaptive. The key findings of this work are: 1) Explicitly incorporating environmental physical parameter estimation into the controller enhances adaptability and accuracy. 2) Even if estimation is not highly accurate, a fast estimation process still benefits the controller. 3) From a sim-to-real perspective, critical factors vary by the desired accuracy level: controller gain and cycle are crucial for position/velocity accuracy, while model structure and parameter accuracy are vital for torque-level precision. 4) In teleoperation, applying estimation as haptic feedback or shared autonomy improves task performance and reduces the operator’s workload, especially in complex, fast-paced scenarios.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Baek, Donghoon

Contributors dc:contributor

• Ramos, Joao
• Dullerud, Geir
• Kim, Joohyung
• Yim, Justin
• Li, Yunzhu

• Humanoid
• Teleoperation
• Parameter Estimation
• Sim-to-real

Statement dc:rights

• Copyright 2025 Donghoon Baek

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130123

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130123