Minimum uncertainty latent variable models for robot recognition of sequential human activities

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2017, Pages 2592-2599

  Han, Fei ^a   Reardon, Christopher ^b   Parker, Lynne E ^c   Zhang, Hao ^a  

^a Department of Metallurgical and Materials Engineering   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

^c UNIVERSITY OF TENNESSEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EQUIVALENCE CLASSES; RANDOM PROCESSES; ROBOTICS; ROBOTS; UNCERTAINTY ANALYSIS;

CLOSED FORM SOLUTIONS; CONDITIONAL PROBABILITIES; HIDDEN CONDITIONAL RANDOM FIELDS; HUMAN ACTIVITY RECOGNITION; LATENT VARIABLE MODELS; REGULARIZATION TERMS; SEQUENTIAL ACTIVITIES; STATE-OF-THE-ART PERFORMANCE;

HUMAN ROBOT INTERACTION;

EID: 85017486016     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2017.7989302     Document Type: Conference Paper

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