scholarly journals Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

10.5772/5816 ◽  
2004 ◽  
Vol 1 (4) ◽  
pp. 29 ◽  
Author(s):  
Olivier Aycard ◽  
Jean-Francois Mari ◽  
Richard Washington
Keyword(s):  
Mobile Robots ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Second Order

scholarly journals Second-Order Belief Hidden Markov Models

2014 ◽  
pp. 284-293 ◽  
Author(s):  
Jungyeul Park ◽  
Mouna Chebbah ◽  
Siwar Jendoubi ◽  
Arnaud Martin
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Second Order

Reshaping human intention in Human-Robot Interactions by robot moves

2019 ◽  
Vol 20 (3) ◽  
pp. 530-560
Author(s):  
Akif Durdu ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Mobile Robots ◽  
Hidden Markov Models ◽  
Mobile Agents ◽  
Markov Models ◽  
Hidden Markov ◽  
Experimental Setup ◽  
Low Level ◽  
Human Intention

Abstract This paper outlines the methodology and experiments associated with the reshaping of human intentions based on robot movements within Human-Robot Interactions (HRIs). Although studies on estimating human intentions are well studied in the literature, reshaping intentions through robot-initiated interactions is a new significant branching in the field of HRI. In this paper, we analyze how estimated human intentions can intentionally change through cooperation with mobile robots in real Human-Robot environments. This paper proposes an intention-reshaping system that includes either the Observable Operator Models (OOMs) or Hidden Markov Models (HMMs) to estimate human intention and decide which moves a robot should perform to reshape previously estimated human intentions into desired ones. At the low level, the system needs to track the locations of all mobile agents using cameras. We test our system on videos taken in a real HRI environment that has been developed as our experimental setup. The results show that OOMs are faster than HMMs and both models give correct decisions for testing sequences.

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