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Learning Perception and Control from Rich Interactions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning Perception and Control from Rich Interactions./
作者:	Fang, Kuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	147 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Deep learning. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812864
ISBN:	9798494455710

Learning Perception and Control from Rich Interactions.
Fang, Kuan.

Learning Perception and Control from Rich Interactions. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 147 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Building robotic systems that can perform a wide range of tasks in the real world would require generalizable perception and control. Despite recent advances in deep learning, existing paradigms often rely on extensive human engineering and suffer from limited generalization capability. To handle the desired diversity and complexity in challenging tasks, we would need to further scale up learning by rethinking the methodology for collecting and utilizing data for robots. In this dissertation, we discuss methods that enable robots to enhance perception and control by learning from rich interactions with the environment. We develop structured models and learning algorithms for robots to effectively acquire sensorimotor skills and solve sequential tasks. To scale up learning, we design mechanisms to provide data from various sources. We further propose frameworks that adaptively generate tasks from parameterized task spaces to facilitate curriculum learning and skill discovery.

ISBN: 9798494455710Subjects--Topical Terms:

3554982
Deep learning.

Learning Perception and Control from Rich Interactions.
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