Deep Learning and Sentiment Analysis for Human-Robot Interaction

2018 ◽  
pp. 14-18 ◽  
Author(s):  
Mattia Atzeni ◽  
Diego Reforgiato Recupero
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Human Robot Interaction ◽  
Robot Interaction

scholarly journals Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction

2021 ◽  
Author(s):  
Soo-Han Kang ◽  
Ji-Hyeong Han
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Vision System ◽  
Robot Vision ◽  
Learning Model ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Video Captioning ◽  
Global Action ◽  
Deep Learning Model

AbstractRobot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system

scholarly journals Emotion Analysis in Human–Robot Interaction

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1761
Author(s):  
Martina Szabóová ◽  
Martin Sarnovský ◽  
Viera Maslej Krešňáková ◽  
Kristína Machová
Keyword(s):  
Sentiment Analysis ◽  
Human Robot Interaction ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Human Being ◽  
Emotion Classification ◽  
Robot Interaction ◽  
Text Data ◽  
Emotion Analysis ◽  
Lower Accuracy

This paper connects two large research areas, namely sentiment analysis and human–robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human–robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human–robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak.

Robot Vision to Audio Description Based on Deep Learning for Effective Human-Robot Interaction

2019 ◽  
Vol 14 (1) ◽  
pp. 22-30
Author(s):  
Dongkeon Park ◽  
◽  
Kyeong-Min Kang ◽  
Jin-Woo Bae ◽  
Ji-Hyeong Han
Keyword(s):  
Deep Learning ◽  
Robot Vision ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Audio Description

scholarly journals Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction

2020 ◽  
Vol 53 (5) ◽  
pp. 750-755
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Deep Learning ◽  
Human Robot Interaction ◽  
Robot Interaction
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