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

Robotic Pick-and-Place of Partially Visible and Novel Objects.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robotic Pick-and-Place of Partially Visible and Novel Objects./
作者:	Gualtieri, Marcus.
面頁冊數:	1 online resource (136 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649282click for full text (PQDT)
ISBN:	9798534690026

Robotic Pick-and-Place of Partially Visible and Novel Objects.
Gualtieri, Marcus.

Robotic Pick-and-Place of Partially Visible and Novel Objects. - 1 online resource (136 pages)

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

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

Includes bibliographical references

If robots are to be capable of performing tasks in uncontrolled, natural environments, they must be able to handle objects they have never seen before, i.e., novel objects. We study the problem of grasping a partially visible, novel object and placing it in a desired way, e.g., placing a bottle upright onto a coaster. There are two main approaches to this problem: policy learning, where a direct mapping from observations to actions is learned, and modular systems, where a perceptual module predicts the objects' geometry and a planning module calculates a sequence of grasps and places valid for the perceived geometry. We have two contributions. The first relates to policy learning. We develop efficient mechanisms for sampling six degree-of-freedom gripper poses. Efficient sampling enables the use of established value-based reinforcement learning algorithms for pick-and-place of novel objects. Our second contribution relates to modular systems. We show that perceptual uncertainty is relevant to regrasping performance, and we compare different ways of incorporating perceptual uncertainty into the regrasp planning cost. Overall, we increase the range of objects robots can pick-and-place reliably without human intervention. This gets us a step closer to robots that work outside of factories and laboratories, i.e., in uncontrolled environments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798534690026Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

542853
Electronic books.

Robotic Pick-and-Place of Partially Visible and Novel Objects.
LDR:02652nmm a2200373K 4500 001 2357221
005 20230622065019.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798534690026
035 $a (MiAaPQ)AAI28649282
035 $a AAI28649282
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Gualtieri, Marcus. $3 3697751
245 1 0 $a Robotic Pick-and-Place of Partially Visible and Novel Objects.
264 0 $c 2021
300 $a 1 online resource (136 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Platt, Robert.
502 $a Thesis (Ph.D.)--Northeastern University, 2021.
504 $a Includes bibliographical references
520 $a If robots are to be capable of performing tasks in uncontrolled, natural environments, they must be able to handle objects they have never seen before, i.e., novel objects. We study the problem of grasping a partially visible, novel object and placing it in a desired way, e.g., placing a bottle upright onto a coaster. There are two main approaches to this problem: policy learning, where a direct mapping from observations to actions is learned, and modular systems, where a perceptual module predicts the objects' geometry and a planning module calculates a sequence of grasps and places valid for the perceived geometry. We have two contributions. The first relates to policy learning. We develop efficient mechanisms for sampling six degree-of-freedom gripper poses. Efficient sampling enables the use of established value-based reinforcement learning algorithms for pick-and-place of novel objects. Our second contribution relates to modular systems. We show that perceptual uncertainty is relevant to regrasping performance, and we compare different ways of incorporating perceptual uncertainty into the regrasp planning cost. Overall, we increase the range of objects robots can pick-and-place reliably without human intervention. This gets us a step closer to robots that work outside of factories and laboratories, i.e., in uncontrolled environments.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
650 4 $a Robotics. $3 519753
650 4 $a Robots. $3 529507
650 4 $a Euclidean space. $3 3562319
650 4 $a Algorithms. $3 536374
650 4 $a Success. $3 518195
650 4 $a Heuristic. $3 568476
650 4 $a Planning. $3 552734
650 4 $a Learning. $3 516521
650 4 $a Neural networks. $3 677449
650 4 $a Sensors. $3 3549539
653 $a Machine learning
653 $a Manipulation
653 $a Pick-and-place
653 $a Robotics
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
690 $a 0771
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Northeastern University. $b Computer Science. $3 1678818
773 0 $t Dissertations Abstracts International $g 83-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28649282 $z click for full text (PQDT)