Wyniki wyszukiwania - BazTech

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Social robot response to negative emotions as a PDDL planning problem in the presence of uncertainty

Probierz Eryka, Gałuszka Anita, Gałuszka Adam

Przegląd Elektrotechniczny

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2023

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R. 99, nr 8

33--37

EN

We propose to use automatic scheduling in the presence of uncertainty methodology to analyze the emotional state of a person and possible responses of a social robot. The emotions considered were: Sadness, Fear, Anger, Disgust and Contempt. The scenarios considered include modelling uncertainty in emotion detection. The result of the work is a set of two planning domains with illustrative examples. It was assumed that when negative emotions are detected, the robot should react in such a way as to reduce or not escalate them.

PL

Proponujemy wykorzystanie metodologii automatycznego planowania w obecności niepewności do analizy stanu emocjonalnego osoby i możliwych reakcji robota społecznego. Rozważane emocje to: Smutek, Strach, Złość, Obrzydzenie i Pogarda. Rozważane scenariusze obejmują modelowanie niepewności w detekcji emocji. Efektem pracy jest zestaw dwóch domen planistycznych wraz z ilustrującymi je przykładami. Założono, że w przypadku wykrycia negatywnych emocji robot powinien reagować w taki sposób, aby je zmniejszyć lub nie eskalować.

2

‘It is really interesting how that small robot impacts humans.’ the exploratory analysis of human attitudes toward the social robot vector in reddit and youtube comments

Łupkowski Paweł, Danilewicz Olga, Ratajczyk Dawid, Wasielewska Aleksandra

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 2

3--10

EN

We present the results of an exploratory analysis of hu‐ man attitudes toward the social robot Vector. The study was conducted on natural language data (2,635 com‐ ments) retrieved from Reddit and YouTube. We describe the tag‐set used and the (manual) annotation procedure. We present and compare attitude structures mined from Reddit and YouTube data. Two main findings are descri‐ bed and discussed: almost 20% of comments from both Reddit and YouTube consist of various manifestations of attitudes toward Vector (mainly attribution of autonomy and declaration of feelings toward Vector); Reddit and YouTube comments differ when it comes to revealed at‐ titude structure – the data source matters for attitudes studies.

3

Hubero : a framework to simulate human behaviour in robot research

Karwowski Jarosław, Dudek Wojciech, Węgierek Maciej, Winiarski Tomasz

Journal of Automation Mobile Robotics and Intelligent Systems

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2021

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Vol. 15, No. 1

31--38

EN

Social robots’ software is commonly tested in a simula‐ tion due to the safety and convenience reasons as well as an environment configuration repeatability assurance. An interaction between a robot and a human requires ta‐ king a person presence and his movement abilities into consideration. The purpose of the article is to present the HuBeRo framework, which can be used to simulate hu‐ man motion behaviour. The framework allows indepen‐ dent control of each individual’s activity, which distinguis‐ hes the presented approach from state‐of‐the‐art, open‐ source solutions from the robotics domain. The article presents the framework assumptions, architecture, and an exemplary application with respect to presented sce‐ narios.

4

Evaluation of the uncanny valley hypothesis based on declared emotional response and psychophysiological reaction

Ratajczyk Dawid, Jukiewicz Marcin, Lupkowski Paweł

Bio-Algorithms and Med-Systems

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2019

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Vol. 15, no. 2

art. no. 20190008

EN

The uncanny valley (UV) hypothesis suggests that the observation of almost human-like characters causes an increase of discomfort. We conducted a study using self-report questionnaire, response time measurement, and electrodermal activity (EDA) evaluation. In the study, 12 computer-generated characters (robots, androids, animated, and human characters) were presented to 33 people (17 women) to (1) test the effect of a background context on the perception of characters, (2) establish whether there is a relation between declared feelings and physiological arousal, and (3) detect the valley of the presented stimuli. The findings provide support for reverse relation between human-likeness and the arousal (EDA). Furthermore, a positive correlation between EDA and human-likeness appraisal reaction time upholds one of the most common explanations of the UV - the categorization ambiguity. The absence of the significant relationship between declared comfort and EDA advocates the necessity of physiological measures for UV studies.

5

RGB-D Sensors in Social Robotics

Dziergwa M., Kaczmarek P., Kędzierski J.

Journal of Automation Mobile Robotics and Intelligent Systems

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2015

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Vol. 9, No. 1

18--27

EN

This article presents the results of a series of experiments carried out using a Kinect for Windows sensor coupled with dedicated software. The focus of this study is on the use of such devices in the field of social robotics. Two software packages are considered - Microsoft Kinect SDK and OpenNI coupled with NiTE library. Particular emphasis is placed on the parameters affecting the social competencies of a robot, such as the speed of detecting users, the accuracy of establishing position and orientation of a user or stability of the tracking process. Key characteris tics of the evaluated software packages are identified and differences regarding their usage outlined in view of interaction oriented algorithms.

6

Extensive feature set approach in facial expression recognition in static images

Żarkowski M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2013

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Vol. 7, No. 4

52--58

EN

The article presents the preliminary concept of facial emotion recognition system. The approach focuses on the feature extraction and selection process which simplifies the stage of defining and adding new elements to the feature set. The evaluation of the system was performed with two discriminant analysis classifiers, decision tree classifier and four variants of k-nearest neighbors classifier. The system recognizes seven emotions. The verification step utilizes two databases of face images representing laboratory and natural conditions. Personal and interpersonal emotion recognitin was evaluated. The best quality of classification for personal emotion recognition was achieved by 1NN classifier, the recognition rate was 99.9% for the laboratory conditions and 97.3 for natural conditions. For interpersonal emotion recognition the rate was 82.5%.