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Fusion For Robot Perception and Control.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fusion For Robot Perception and Control./
作者:	Lee, Michelle A.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	166 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Robots. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688326
ISBN:	9798544203520

Fusion For Robot Perception and Control.
Lee, Michelle A.

Fusion For Robot Perception and Control. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 166 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

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

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

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.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.

ISBN: 9798544203520Subjects--Topical Terms:

529507
Robots.

Fusion For Robot Perception and Control.
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