東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

On Using Formal Methods for Safe and Robust Robot Autonomy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On Using Formal Methods for Safe and Robust Robot Autonomy./
作者:	Leung, Karen Yan Ming.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	156 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Robots. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812906
ISBN:	9798494462374

On Using Formal Methods for Safe and Robust Robot Autonomy.
Leung, Karen Yan Ming.

On Using Formal Methods for Safe and Robust Robot Autonomy. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 156 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.

Advances in the fields of artificial intelligence and machine learning have unlocked a new generation of robotic systems---"learning-enabled" robots that are designed to operate in unstructured, uncertain, and unforgiving environments, especially settings where robots are required to interact in close proximity with humans. However, as learning-enabled methods, especially "deep" learning, continue to become more pervasive throughout the autonomy stack, it also becomes increasingly difficult to ascertain the performance and safety of these robotic systems and explain their behavior, necessary prerequisites for their deployment in safety-critical settings. This dissertation develops methods drawing upon techniques from the field of formal methods, namely Hamilton-Jacobi (HJ) reachability and Signal Temporal Logic (STL), to complement a learning-enabled robot autonomy stack, thereby leading to safer and more robust robot behavior. The first part of this dissertation investigates the problem of providing safety assurance for human-robot interactions, safety-critical settings wherein robots must reason about the uncertainty in human behavior to achieve seamless interactions with humans. Specifically, we develop a two-step approach where we first develop a learning-based human behavior prediction model tailored towards proactive robot planning and decision-making, which we then couple with a reachability-based safety controller that minimally intervenes whenever the robot is near safety violation. The approach is validated through human-in-the-loop simulation as well as on an experimental vehicle platform, demonstrating clear connections between theory and practice. The second part of this dissertation examines the use of STL as a formal language to incorporate logical reasoning into robot learning. In particular, we develop a technique, named STLCG, that casts STL into the same computational language as deep neural networks. Consequently, by using STLCG to express designers' domain expertise into a form compatible with neural networks, we can embed domain knowledge into learned components within the autonomy stack to provide additional levels of robustness and interpretability.

ISBN: 9798494462374Subjects--Topical Terms:

529507
Robots.

On Using Formal Methods for Safe and Robust Robot Autonomy.
LDR:03299nmm a2200349 4500 001 2344914
005 20220531062213.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494462374
035 $a (MiAaPQ)AAI28812906
035 $a (MiAaPQ)STANFORDkk533yj7758
035 $a AAI28812906
040 $a MiAaPQ $c MiAaPQ
100 1 $a Leung, Karen Yan Ming. $3 3683749
245 1 0 $a On Using Formal Methods for Safe and Robust Robot Autonomy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 156 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Pavone, Marco;Gerdes, J. ;Schwager, Mac.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Advances in the fields of artificial intelligence and machine learning have unlocked a new generation of robotic systems---"learning-enabled" robots that are designed to operate in unstructured, uncertain, and unforgiving environments, especially settings where robots are required to interact in close proximity with humans. However, as learning-enabled methods, especially "deep" learning, continue to become more pervasive throughout the autonomy stack, it also becomes increasingly difficult to ascertain the performance and safety of these robotic systems and explain their behavior, necessary prerequisites for their deployment in safety-critical settings. This dissertation develops methods drawing upon techniques from the field of formal methods, namely Hamilton-Jacobi (HJ) reachability and Signal Temporal Logic (STL), to complement a learning-enabled robot autonomy stack, thereby leading to safer and more robust robot behavior. The first part of this dissertation investigates the problem of providing safety assurance for human-robot interactions, safety-critical settings wherein robots must reason about the uncertainty in human behavior to achieve seamless interactions with humans. Specifically, we develop a two-step approach where we first develop a learning-based human behavior prediction model tailored towards proactive robot planning and decision-making, which we then couple with a reachability-based safety controller that minimally intervenes whenever the robot is near safety violation. The approach is validated through human-in-the-loop simulation as well as on an experimental vehicle platform, demonstrating clear connections between theory and practice. The second part of this dissertation examines the use of STL as a formal language to incorporate logical reasoning into robot learning. In particular, we develop a technique, named STLCG, that casts STL into the same computational language as deep neural networks. Consequently, by using STLCG to express designers' domain expertise into a form compatible with neural networks, we can embed domain knowledge into learned components within the autonomy stack to provide additional levels of robustness and interpretability.
590 $a School code: 0212.
650 4 $a Robots. $3 529507
650 4 $a Temporal logic. $3 3681666
650 4 $a Planning. $3 552734
650 4 $a Neural networks. $3 677449
650 4 $a Cognition & reasoning. $3 3556293
650 4 $a Artificial intelligence. $3 516317
650 4 $a Cognitive psychology. $3 523881
650 4 $a Logic. $3 529544
650 4 $a Psychology. $3 519075
650 4 $a Robotics. $3 519753
690 $a 0800
690 $a 0633
690 $a 0395
690 $a 0621
690 $a 0771
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812906