Title:
Machine Learning for Agile Robotic Control
Machine Learning for Agile Robotic Control
Author(s)
Wagener, Nolan C.
Advisor(s)
Boots, Byron
Tsiotras, Panagiotis
Tsiotras, Panagiotis
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Abstract
Roboticists typically exploit structure in a problem, such as by modeling the mechanics of a system, to generate solutions for a given task. However, this structure can limit flexibility and require practitioners to reason about challenging phenomena, such as contacts in mechanics. Data, conversely, provides much more flexibility and, when combined with deep neural networks, has given rise to powerful models in vision and language, all with little hand-engineered structure. While it is tempting to fully forego structure in favor of learning-based methods for robotics, we show how data and learning can be gracefully incorporated in a structured way. In particular, we focus on the control setting, and we demonstrate that robotic control offers a variety of modes that data can be utilized. First, we show that data can be used in a model-based fashion to train a neural network that approximates complex dynamics and which can be used within a model predictive controller (MPC). Then, we show that the MPC process is itself an instance of online learning and demonstrate how to synthesize MPC algorithms from a common online learning algorithm. We apply both of the aforementioned approaches on a real-world aggressive driving task and show that they can accomplish the task. Next, we consider the safe reinforcement learning problem and show that safety interventions can be used as a learning signal to have an agent learn to become safe without needing to execute unsafe actions in the environment. Finally, we consider the simulated humanoid domain and show that pre-collected human motions can act as a strong inductive bias to ground motions learned by the humanoid agent.
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Date Issued
2023-12-06
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Resource Type
Text
Resource Subtype
Dissertation