Fusion for robot perception and control
Abstract/Contents
- Abstract
- Humans have long dreamed of robots that can perform a wide variety of tasks, such as cooking, cleaning, and exploring potentially dangerous environments. However, robotics adoption still struggles even in highly-structured environments. In factories, robots currently account for less than one third of the manufacturing workforce. Because many robots need to be hardcoded for every task, they often cannot deal with any errors in their models nor any changes to the environment. In academic research, recent works in machine learning are enabling robots to learn directly from data. Particularly in the areas of learning-based perception and control, we see advancements in deep learning for visual perception from raw images as well as deep reinforcement learning (RL) for learning complex skills from trial and error. However, these black-box techniques often require large amounts of data, have difficult-to-interpret results and processes, and fail catastrophically when dealing with out-of-distribution data. In order to create robotic systems that can flexibly operate in dynamic environments, we want robot perception and control algorithms that have three characteristics: sample efficiency, robustness, and generalizability. In this dissertation, I introduce the concept of ''fusion'' in robot perception and control algorithms to achieve these three characteristics. On the perception side, we fuse multiple sensor modalities and demonstrate generalization to new task instances and robustness to sensor failures. On the control side, we leverage fusion by combining known models with learned policies, making our policy learning substantially more sample efficient.
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 | Lee, Michelle Annabel |
|---|---|
| Degree supervisor | Bohg, Jeannette, 1981- |
| Degree supervisor | Okamura, Allison |
| Thesis advisor | Bohg, Jeannette, 1981- |
| Thesis advisor | Okamura, Allison |
| Thesis advisor | Li, Fei Fei, 1976- |
| Degree committee member | Li, Fei Fei, 1976- |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Michelle A. Lee. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/pw700fk3337 |
Access conditions
- Copyright
- © 2021 by Michelle Annabel Lee
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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