USING MACHINE LEARNING TECHNIQUES IN REAL-WORLD MOBILE ROBOTS

被引：2
|
作者：
KAISER, M
KLINGSPOR, V
MILLAN, JDR
ACCAME, M
WALLNER, F
DILLMANN, R
机构：
[1] UNIV DORTMUND,FACHBEREICH INFORMAT,LEHRSTUHL 8,D-44221 DORTMUND,GERMANY
[2] COMMISS EUROPEAN COMMUNITIES,JOINT RES CTR,INST SYST ENGN & INFORMAT,I-21020 ISPRA,ITALY
来源：
IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS | 1995年 / 10卷 / 02期
关键词：
D O I：
10.1109/64.395353
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
APPLYING MACHINE LEARNING TECHNIQUES CAN HELP MOBILE ROBOTS MEET THE NEED FOR INCREASED SAFETY AND ADAPTIVITY THAT REAL-WORLD OPERATION DEMANDS. THE TECHNIQUES ALSO FACILITATE ROBOT-TO-USER COMMUNICATION.
引用
收藏
页码：37 / 45
页数：9
相关论文
共 50 条
  • [21] Robots for Real: Developing a Participatory Design Framework for Implementing Educational Robots in Real-World Learning Environments
    Bertel, Lykke Brogaard
    Rasmussen, Dorte Malig
    Christiansen, Ellen
    HUMAN-COMPUTER INTERACTION - INTERACT 2013, PT II, 2013, 8118 : 437 - 444
  • [22] Exploration of machine learning techniques to examine the journey to neuroendocrine tumor diagnosis with real-world data
    Zimmerman, Nicole M.
    Ray, David
    Princic, Nicole
    Moynihan, Meghan
    Clarke, Callisia
    Phan, Alexandria
    FUTURE ONCOLOGY, 2021, 17 (24) : 3217 - 3230
  • [23] AN INNOVATIVE TOOL BASED ON MACHINE LEARNING TECHNIQUES PREDICTS NASH PATIENTS IN REAL-WORLD SETTINGS
    Huang, J.
    Doherty, M.
    Regnier, S.
    Capkun, G.
    Balp, M.
    Ye, Q.
    Janssens, N.
    Lopez, P.
    Pedrosa, M.
    Schattenberg, Joern
    HEPATOLOGY, 2019, 70 : 124A - 124A
  • [24] Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence
    Zheng, Ping
    Yu, Ze
    Li, Liren
    Liu, Shiting
    Lou, Yan
    Hao, Xin
    Yu, Peng
    Lei, Ming
    Qi, Qiaona
    Wang, Zeyuan
    Gao, Fei
    Zhang, Yuqing
    Li, Yilei
    FRONTIERS IN PHARMACOLOGY, 2021, 12
  • [25] Real-World Evidence: Integrating Machine Learning with Real-World Big Data for Predictive Analytics in Healthcare
    Vecchio, Nicolas
    CARDIOLOGY, 2024,
  • [26] Cognitive robots learning failure contexts through real-world experimentation
    Karapinar, Sertac
    Sariel, Sanem
    AUTONOMOUS ROBOTS, 2015, 39 (04) : 469 - 485
  • [27] Cognitive robots learning failure contexts through real-world experimentation
    Sertac Karapinar
    Sanem Sariel
    Autonomous Robots, 2015, 39 : 469 - 485
  • [28] Mobile robots facing the real world
    Siegwart, Roland
    FIELD AND SERVICE ROBOTICS: RECENT ADVANCES IN RESEARCH AND APPLICATIONS, 2006, 24 : 21 - 30
  • [29] Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications
    Shawen, Nicholas
    Lonini, Luca
    Mummidisetty, Chaithanya Krishna
    Shparii, Ilona
    Albert, Mark V.
    Kording, Konrad
    Jayaraman, Arun
    JMIR MHEALTH AND UHEALTH, 2017, 5 (10):
  • [30] Real-World Parkinson's Hand Tremor Detection Using Ensemble Learning Techniques
    Hur, Sungwook
    Zhang, Jieming
    Kim, Moon-Hyun
    Chung, Tai-Myoung
    IEEE ACCESS, 2025, 13 : 22678 - 22693
12345