A Sequential Optimisation Framework for Adaptive Model Predictive Control in Robotics
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Guzman Apaza, RelAbstract
State-of-the-art control and robotics challenges have long been tackled using model-based control methods like model predictive control (MPC) and reinforcement learning (RL). These methods excel in complex dynamic domains, such as manipulation tasks, but struggle with real-world ...
See moreState-of-the-art control and robotics challenges have long been tackled using model-based control methods like model predictive control (MPC) and reinforcement learning (RL). These methods excel in complex dynamic domains, such as manipulation tasks, but struggle with real-world issues like wear-and-tear, uncalibrated sensors, and misspecifications. These factors often perturb system dynamics, leading to the 'reality gap' problem when robots transition from simulations to real-world environments. This work aims to bridge this gap by combining RL and control in a learning framework that adapts MPC to robot decisions, optimizing performance despite uncertainties in dynamics model parameters. This thesis presents three key contributions to robotics control. The first is a novel reward-based framework for refining stochastic Model Predictive Control (MPC). It utilizes Bayesian Optimization (BO) for efficient data handling and heteroscedastic noise, linking controller hyperparameters with expected rewards through a Gaussian Process (GP). This approach demonstrates success in simulated control environments and robotic tasks. The second contribution addresses the 'reality gap' in robotics, enhancing controller performance in real-world dynamics. It builds on the first by developing an adaptive stochastic MPC that optimizes hyperparameters while estimating physical parameter distributions, employing a randomized dynamics model. This method is validated in both simulations and with robotic manipulators. Finally, the thesis proposes an innovative alternative to BO, merging it with supervised classification for a surrogate-based optimization technique. This method adeptly adjusts control hyperparameters in the face of model uncertainty and noise, optimizing complex functions through a binary classifier. Tested on simulated control problems and manipulators, it offers a promising solution to complex robotics and control challenges.
See less
See moreState-of-the-art control and robotics challenges have long been tackled using model-based control methods like model predictive control (MPC) and reinforcement learning (RL). These methods excel in complex dynamic domains, such as manipulation tasks, but struggle with real-world issues like wear-and-tear, uncalibrated sensors, and misspecifications. These factors often perturb system dynamics, leading to the 'reality gap' problem when robots transition from simulations to real-world environments. This work aims to bridge this gap by combining RL and control in a learning framework that adapts MPC to robot decisions, optimizing performance despite uncertainties in dynamics model parameters. This thesis presents three key contributions to robotics control. The first is a novel reward-based framework for refining stochastic Model Predictive Control (MPC). It utilizes Bayesian Optimization (BO) for efficient data handling and heteroscedastic noise, linking controller hyperparameters with expected rewards through a Gaussian Process (GP). This approach demonstrates success in simulated control environments and robotic tasks. The second contribution addresses the 'reality gap' in robotics, enhancing controller performance in real-world dynamics. It builds on the first by developing an adaptive stochastic MPC that optimizes hyperparameters while estimating physical parameter distributions, employing a randomized dynamics model. This method is validated in both simulations and with robotic manipulators. Finally, the thesis proposes an innovative alternative to BO, merging it with supervised classification for a surrogate-based optimization technique. This method adeptly adjusts control hyperparameters in the face of model uncertainty and noise, optimizing complex functions through a binary classifier. Tested on simulated control problems and manipulators, it offers a promising solution to complex robotics and control challenges.
See less
Date
2023Licence
Copyright All Rights ReservedRights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Civil EngineeringAwarding institution
The University of SydneyShare