國立虎尾科技大學 |

Learning to Manipulate Novel Objects for Assistive Robots.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Learning to Manipulate Novel Objects for Assistive Robots./
作者:	Sung, Jaeyong.
面頁冊數:	1 online resource (190 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780355026610

Learning to Manipulate Novel Objects for Assistive Robots.
Sung, Jaeyong.

Learning to Manipulate Novel Objects for Assistive Robots. - 1 online resource (190 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Cornell University, 2017.

Includes bibliographical references

The ability to reason about different modalities of information, for the purpose of physical interaction with objects, is a critical skill for assistive robots. For a robot to be able to assist us in our daily lives, it is not feasible to train each robot for a large number of tasks with all instances of objects that exist in human environments. Robots will have to generalize their skills by jointly reasoning with various sensor modalities such as vision, language and haptic feedback. This is an extremely challenging problem because each modality has intrinsically different statistical properties. Moreover, even with expert knowledge, manually designing joint features between such disparate modalities is difficult.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355026610Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Learning to Manipulate Novel Objects for Assistive Robots.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Ashutosh Saxena.
502 $a Thesis (Ph.D.)--Cornell University, 2017.
504 $a Includes bibliographical references
520 $a The ability to reason about different modalities of information, for the purpose of physical interaction with objects, is a critical skill for assistive robots. For a robot to be able to assist us in our daily lives, it is not feasible to train each robot for a large number of tasks with all instances of objects that exist in human environments. Robots will have to generalize their skills by jointly reasoning with various sensor modalities such as vision, language and haptic feedback. This is an extremely challenging problem because each modality has intrinsically different statistical properties. Moreover, even with expert knowledge, manually designing joint features between such disparate modalities is difficult.
520 $a In this dissertation, we focus on developing learning algorithms for robots that model tasks involving interactions with various objects in unstructured human environments---especially on novel objects and scenarios that involve sequences of complicated manipulation. To this end, we develop algorithms that learn shared representations of multimodal data and model full sequences of complex motions. We demonstrate our approach on several different applications: understanding human activities in unstructured environment, synthesizing manipulation sequences for under-specified tasks, manipulating novel appliances, and manipulating objects with haptic feedback.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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