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Early Turn-Taking Prediction for Human Robot Collaboration
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Early Turn-Taking Prediction for Human Robot Collaboration. |
| 개인저자 | Zhou, Tian. |
| 단체저자명 | Purdue University. Industrial Engineering. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 147 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438368996 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Juan P. Wachs. |
| 요약 | To enable natural and fluent human robot collaboration, it is critical for a robot to comprehend their human partners' on-going actions, predict their behaviors in the near future, and plan its actions accordingly. Specifically, the capability o |
| 요약 | To that end, this dissertation presents the design and implementation of an early turn-taking prediction framework, centered around physical human robot collaboration tasks. The prediction framework leverages multimodal communication cues (both |
| 요약 | The developed framework was evaluated in two important scenarios, the first one is healthcare where a robotic scrub nurse delivers surgical instruments to surgeons in the operating room. The second one is manufacturing where a robotic assembly a |
| 일반주제명 | Robotics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
