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Within the present research, nine different communication strategies were examined for a service robot. The communication style (humanlike friendly, humanlike professional, machinelike) and modality (auditive male, auditive female, visual) of communication was varied and their influence on system evaluation regarding competence, warmth, discomfort, embodiment and preferences were investigated in an online study. The results of the study indicate that a humanlike communication is rated more competent, warmer, less discomforting, and is generally preferred.

IEEE RO-MAN 2021

“Oh no, my instructions were wrong!” An Exploratory Pilot Towards Children’s Trust in Social Robots

machinelike

humanlike

communication strategies

modality

gender

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RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

We focused on neonatal resuscitation, which is difficult to train clinically, and our long- term goal is a neonatal resuscitation training system that can simulate cases and can feedback interactively based on quantitative evaluation of the procedures. In this study, we developed the neonatal simulator that has a respiratory movement simulating mechanism and a chest compression measurement mechanism. Through an interview with neonatologists, the fidelity of respiratory movements and the possibility of evaluating chest compression with the developed simulator were assessed.

Robocentric Conversational Group Discovery

This research has developed a new method for predicting natural human arm movements. It introduces the five-parameter logistic function into this application space and uses supervised learning to predict fingertip trajectories and elbow swivel angles. The method presents small error in an experimental study, improving upon previous methods and verifying its effectiveness.

Human Arm Motion Prediction in Reaching Movements

We present a 3D human activity simulator, ``HOIsim", focusing on Human-Object Interactions (HOIs). Using HOIsim we model two complex activities for ``lunch" and ``breakfast". Furthermore, we introduce methods to meaningfully randomize activity flows and the environment topology, and make a dataset of the 3D pose maps publicly available.

HOIsim: Synthesizing Realistic 3D Human-Object Interaction Data for Human Activity Recognition

This work presents a method for recognition of walking activities and prediction of gait periods using wearable sensors. First, a Convolutional Neural Network (CNN) is used to recognise the walking activity and gait period. Second, the output of the CNN is used by a Predicted Information Gain (PIG) method to predict the next most probable gait period while walking. The output of these two processes are combined to adapt the recognition accuracy of the system. This adaptive combination allows us to achieve an optimal recognition accuracy over time.

Parents’ Views on Using Social Robots for Language Learning

Gesture are a natural modality of human interaction. Smart technologies are seamlessly part of our daily lives, becoming essential to endow them with gesture recognition capbilities to ensure a natural interaction. This work leverages the power of convolutional neural networks proposinga novel spatiotemporal visually interppretable image encoding representation of skeleton data for CNN-based gesture recognition.

Robotic Object Sorting via Deep Reinforcement Learning: a Generalized Approach

In a world where applications of human-robot interaction are beneficial for our society in many aspects, robots sometimes need to mimic our behavior and body expressions. To achieve this, we present a novel framework for motion retargeting (i.e., transferring human motion) to humanoid robots by implementing pose estimation, an efficient and practical method of motion capture from just videos of the human based on computer vision and deep learning approaches.

A Bistable Soft Gripper with Mechanically Embedded Sensing and Actuation for Fast Grasping

This paper argues for the possibility of e-friendship, a form of friendship between humans and silicon-based electronic agents, with a focus on sociable robots. Section I introduces the topic. Section II defines "e-friendship." Sections III and IV identify qualities humans and silicon-based systems require for e-friendship. Section V defends the proposal against objections.

Exercising with an “Iron Man”: Design for a Robot Exercise Coach for Persons with Dementia

A critical issue during HRI is eliciting an intentional stance in the human interactor, who then perceive the agent as an "intelligent" being with full agency towards their own intentions and desires. Eliciting such a stance, however, has proven elusive. We argue for a paradigm shift in our approach to this problem, based on recent evidence from social robotics and digital avatars. To trigger an intentional stance in humans, perhaps our artificial agents need to adopt one about themselves.

What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

We aim to improve the believability of virtual players by incorporating personalities, moods, and emotions for them. We propose a novel framework for a multi-player VR Volleyball game by integrating various behavioral changes during gameplay. This is achieved by simulating player reactions and rendering changes in the player's body postures, which are dependent on game environment parameters and scenarios. We showcase visual analysis of individual players' mood changes during gameplay to further corroborate the presented VR framework. Incorporating the proposed model, we exhibit the progression and variations in moods for players with different personalities when presented with varied game situations.

Rate My Robot: The effect of Word-of-Mouth (WOM) on perceptions of a social robot’s teaching performance

We tested a new method for presenting natural materials using an electrostatic tactile texture display. In this method, we combined Coulomb friction as physical model for low frequency friction and texture-induced vibration model for high frequency friction. In a condition where only the low-frequency friction force components were rendered, the materials were correctly recognized at 70%. In contrast, when the high-frequency components were superposed, this rate increased to 80%. Therefore, Coulomb friction model plus fine texture improves material discrimination accuracy.

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“Oh no, my instructions were wrong!” An Exploratory Pilot Towards Children’s Trust in Social Robots

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