| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330418 |
| 005 | | 20241029174214 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438368996 |
| 035 | |
▼a (MiAaPQ)AAI10844730 |
| 035 | |
▼a (MiAaPQ)purdue:23214 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 629.8 |
| 100 | 1 |
▼a Zhou, Tian. |
| 245 | 10 |
▼a Early Turn-Taking Prediction for Human Robot Collaboration. |
| 260 | |
▼a [S.l.] :
▼b Purdue University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 147 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Juan P. Wachs. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Purdue University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0183. |
| 650 | 4 |
▼a Robotics. |
| 690 | |
▼a 0771 |
| 710 | 20 |
▼a Purdue University.
▼b Industrial Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0183 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000010
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |