A Method of Intention Estimation for Human-Robot Interaction

2019 ◽  
pp. 69-80
Author(s):  
Jing Luo ◽  
Chao Liu ◽  
Ning Wang ◽  
Chenguang Yang
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Intention Estimation

Robotic Hardware and Software Integration for Changing Human Intentions

2012 ◽  
pp. 380-406 ◽  
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

scholarly journals Robotic Hardware and Software Integration for Changing Human Intentions

Robotics ◽  
2013 ◽  
pp. 1381-1406
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

scholarly journals Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095364
Author(s):  
Lan Ye ◽  
Genliang Xiong ◽  
Cheng Zeng ◽  
Hua Zhang
Keyword(s):  
External Force ◽  
Trajectory Tracking ◽  
Human Robot Interaction ◽  
System Stability ◽  
Tracking Accuracy ◽  
Compliance Control ◽  
Robot Interaction ◽  
Redundant Robot ◽  
Virtual Force ◽  
Intention Estimation

Collaborative robot has been widespread application prospect, such as homes, manufacturing, and health-care etc. In physical human-robot interaction, the external force appears inevitably in contact with environment or human, especially the interactive tasks such as trajectory tracking requirements and force compliance control. In this article, a method based on interaction intention estimation, which solve the problem of trajectory tracking accuracy and force compliance control in the same direction for the 7-DOF robot, is proposed. The increased virtual force depended on the manipuility performance index and inverse kinematic solution used the kinematic decoupling method based on the redundant angle avoid the singularity of redundant robot. Then, based on interactive intention estimation, a control strategy of variable impedance sliding mode theory in the presence of virtual force and contact force is proposed to achieve the trajectory tracking. We adopted hyperbolic tangent function to alleviate the chattering problem caused by switch function and validated the control system stability by Lyapunov theorem. Finally, Matlab simulations exhibit a 97.8% of high tracking accuracy amid the external force is 43% less than variable impedance parameters. It is therefore proved that the proposed method can achieve asymptotic tracking and the compliant behavior in physical human-robot interaction.

scholarly journals Computational Modeling of Human-Robot Interaction Based on Active Intention Estimation

2008 ◽  
pp. 185-192 ◽  
Author(s):  
Takashi Omori ◽  
Ayami Yokoyama ◽  
Hiroyuki Okada ◽  
Satoru Ishikawa ◽  
Yugo Nagata
Keyword(s):  
Computational Modeling ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Intention Estimation

Engagement Intention Estimation in Multiparty Human-Robot Interaction

2021 ◽  
Author(s):  
Zhijie Zhang ◽  
Jianmin Zheng ◽  
Nadia Magnenat Thalmann
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Intention Estimation

A Comparison of Bystander and Team Member Distance in Human--Robot Interaction

2014 ◽  
Author(s):  
Mitchell S. Dunfee ◽  
Tracy Sanders ◽  
Peter A. Hancock
Keyword(s):  
Team Member ◽  
Human Robot Interaction ◽  
Robot Interaction
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