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Author ORCID Identifier
https://orcid.org/0000-0001-8360-9214
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2019
Month Degree Awarded
September
First Advisor
Roderic A. Grupen
Second Advisor
Joydeep Biswas
Third Advisor
Philip S. Thomas
Fourth Advisor
Weibo Gong
Subject Categories
Artificial Intelligence and Robotics | Computer Sciences
Abstract
Deep learning has been successful in a variety of applications, such as object recognition, video games, and machine translation. Deep neural networks can automatically learn important features given large training datasets. However, the success of deep learning in robotic systems in the real world is still limited mainly because obtaining large datasets and labeling are costly. As a result, much of the successful work in deep learning has been limited to domains where large datasets are readily available or easily collected. To address this issue, I propose a framework for acquiring re-usable skills efficiently combining intrinsic motivation and the control basis framework --- a developmental architecture implemented using a landscape of attractors. A deep neural classifier is used to predict probabilistic control affordances representing controller states accessible by way of actions. Information theoretic motivation with embedding distance influences exploration in a way that supports learning control affordances efficiently. The learned affordance is used as a filter to help a robot experience more positive outcomes of controllers when a robot learns a new affordance. I conduct quantitative experiments using a dynamic simulator and the results show that the proposed learning framework enables a mobile manipulator to learn deep sensorimotor skills more efficiently in a self-supervised learning manner.
DOI
https://doi.org/10.7275/15177045
Recommended Citation
Takahashi, Takeshi, "Efficient Self-supervised Deep Sensorimotor Learning in Robotics" (2019). Doctoral Dissertations. 1769.
https://doi.org/10.7275/15177045
https://scholarworks.umass.edu/dissertations_2/1769
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.