Exploring cultural factors in human-robot interaction: A matter of personality?

This paper proposes an experimental study to investigate task-dependence and cultural-background dependence of the personality trait attribution on humanoid robots. In Human-Robot Interaction, as well as in Human-Agent Interaction research, the attribution of personality traits towards intelligent agents has already been researched intensively in terms of the social similarity or complementary rule. These two rules imply that humans either tend to like others with similar personality traits or complementary personality traits more. Even though state of the art literature suggests that similarity attraction happens for virtual agents, and complementary attraction for robots, there are many contradictions in the findings. We assume that searching the explanation for personality trait attribution in the similarity and complementary rule does not take into account important contextual factors. Just like people equate certain personality types to certain professions, we expect that people may have certain personality expectations depending on the context of the task the robot carries out. Because professions have different social meaning in different national culture, we also expect that these task-dependent personality preferences differ across cultures. Therefore suggest an experiment that considers the task-context and the cultural background of users

Cross-Cultural Understanding of Interface Design: A Cross-Cultural Analysis of Icon Recognition

This paper reports the findings of a small-scale study that investigated cultural aspects of understanding the website of a virtual campus. Results indicate differences in expectations and understanding due to the users’ knowledge of everyday life and real world experience, and suggest that the campus metaphor that was used is not universally transferable

Planning Based System for Child-Robot Interaction in Dynamic Play Environments

This paper describes the initial steps towards the design of a robotic system that intends to perform actions autonomously in a naturalistic play environment. At the same time it aims for social human-robot interaction~(HRI), focusing on children. We draw on existing theories of child development and on dimensional models of emotions to explore the design of a dynamic interaction framework for natural child-robot interaction. In this dynamic setting, the social HRI is defined by the ability of the system to take into consideration the socio-emotional state of the user and to plan appropriately by selecting appropriate strategies for execution. The robot needs a temporal planning system, which combines features of task-oriented actions and principles of social human robot interaction. We present initial results of an empirical study for the evaluation of the proposed framework in the context of a collaborative sorting game

Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning

One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.Comment: Published in the proceedings of INTERSPEECH, Stockholm, September, 201

Learning spectro-temporal features with 3D CNNs for speech emotion recognition

In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed 3D CNNs simultaneously extract short-term and long-term spectral features with a moderate number of parameters. We evaluated our proposed and other state-of-the-art methods in a speaker-independent manner using aggregated corpora that give a large and diverse set of speakers. We found that 1) shallow temporal and moderately deep spectral kernels of a homogeneous architecture are optimal for the task; and 2) our 3D CNNs are more effective for spectro-temporal feature learning compared to other methods. Finally, we visualised the feature space obtained with our proposed method using t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct clusters of emotions.Comment: ACII, 2017, San Antoni

A guide robot at the airport:First impressions

In order to be successful, guide robots in public space require socially-intelligent navigation behaviors. Evaluation of these behaviors can be done through lab studies, though these do not always capture the complexities of interactions in "the wild". In this extended abstract we present initial results of a field trial of a multi-year project in which we developed and deployed a robot which provided guiding services to real passengers at one of the top-20 busiest airports in the world. During this field trial 9 groups of passengers were guided by the robot. We will present initial results and implications for field studies.</p

Useful and motivating robots: the influence of task structure on human-robot teamwork

Robots have recently started to leave their safety cages to be used in close vicinity to humans. This also causes changes in the nature of the tasks that robots and humans solve together, i.e., in the degree of structure of the tasks. While traditional, industrial tasks were highly structured, the new tasks often have a low level of structure. We present a user study that compares a highly and a little structured task in a text-based computer game played by human-robot teams. The results suggest that users do not only find robots useful and motivating in highly structured tasks where they depend on their help, but also in little structured tasks that they could solve on their own

Short duration robot interaction at an airport: challenges from a socio-psychological point of view

This extended abstract concerns the FP7-project Spencer. As part of the Spencer project, a demonstrator robot will be developed which provide services to passengers at a major European airport. Example services include (1) guiding transfer passengers from their arrival gate to the so-called Schengen barrier, and (2) assisting in the transfer process by printing boarding passes. The goal of the robot is to make sure that passengers will make their connecting flight, with our own focus being on the human-robot interaction. In the following, we describe a sample use case of the project scenario. Based on this we identify possible challenges that are of interest with respect to interactive robots in public spaces

Sound over Matter: The Effects of Functional Noise, Robot Size and Approach Velocity in Human-Robot Encounters

In our previous work we introduced functional noise as a modality for robots to communicate intent [6]. In this follow-up experiment, we replicated the first study with a robot which was taller in order to find out if the same results would apply to a tall vs. a short robot. Our results show a similar trend: a robot using functional noise is perceived more positively compared with a robot that does not

Robots sing the body electric: Investigations of body language for social and spatial interaction

No abstract