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Efficient Self-supervised Deep Sensorimotor Learning in Robotics

05 Jun
Wednesday, 06/05/2019 10:00am to 12:00pm
A310 LGRC
Ph.D. Thesis Defense

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 due to the cost of obtaining large datasets.  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. Learned affordances are used as a filter to help a robot experience more convergence outcomes of controllers when a robot learns a new skill. We 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 efficiently in a self-supervised learning manner.

Advisor: Roderic Grupen