Uncertainty and efficiency in adaptive robot learning and control
Abstract/Contents
- Abstract
- Autonomous robots have the potential to free humans from dangerous or dull work. To achieve truly autonomous operation, robots must be able to understand unstructured environments and make safe decisions in the face of uncertainty and non-stationarity. As such, robots must be able to learn about, and react to, changing operating conditions or environments continuously, efficiently, and safely. While the last decade has seen rapid advances in the capabilities of machine learning systems driven by deep learning, these systems are limited in their ability to adapt online, learn with small amounts of data, and characterize uncertainty. The desiderata of learning robots therefore directly conflict with the weaknesses of modern deep learning systems. This thesis aims to remedy this conflict and develop robot learning systems that are capable of learning safely and efficiently. In the first part of the thesis we develop tools for efficient learning in changing environments. In particular, we develop tools for the meta-learning problem setting---in which data from a collection of environments may be used to accelerate learning in a new environment---in both the regression and classification setting. These algorithms are based on exact Bayesian inference on meta-learned features. This approach enables characterization of uncertainty in the face of small amounts of within-environment data, and efficient learning via exact conditioning. We extend these approaches to time-varying settings beyond episodic variation, including continuous gradual environmental variation and sharp, changepoint-like variation. In the second part of the thesis we adapt these tools to the problem of robot modeling and control. In particular, we investigate the problem of combining our neural network-based meta-learning models with prior knowledge in the form of a nominal dynamics model, and discuss design decisions to yield better performance and parameter identification. We then develop a strategy for safe learning control. This strategy combines methods from modern constrained control---in particular, robust model predictive control---with ideas from classical adaptive control to yield a computationally efficient, simple to implement, and guaranteed safe control strategy capable of learning online. We conclude the thesis with a discussion of short, intermediate, and long-term next steps in extending the ideas developed herein toward the goal of true robot autonomy.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Harrison, James Michael |
|---|---|
| Degree supervisor | Okamura, Allison |
| Degree supervisor | Pavone, Marco, 1980- |
| Thesis advisor | Okamura, Allison |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Brunskill, Emma |
| Thesis advisor | Finn, Chelsea |
| Degree committee member | Brunskill, Emma |
| Degree committee member | Finn, Chelsea |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | James Michael Harrison. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/hh754jn1534 |
Access conditions
- Copyright
- © 2021 by James Michael Harrison
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