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Social robots are being introduced in a variety of educational domains with great success. These social robots are often designed by drawing inspiration from practices held by human teachers. Contingent mediations are a prime example of a high-quality teaching practice that supports better outcomes in human-human teaching. This can inform the design of social robots. Current research on robot use in education has focused on curriculum-level contingent mediations where the difficulty level of subsequent tasks are adjusted to the current capabilities of a learner. However, task-level contingent mediations that provide support for students learning during a specific task remain unexplored. This research investigates whether patterns of task-level mediations differ between a robot, virtual, and human agent, as well as their effects on learning outcomes. To investigate these research questions, we designed instruction that utilizes contingent mediational flows, based on formative assessment data, to be delivered by a human, robot, and virtual agent to teach grade 3-5 children science words. We identified 23 unique instructional patterns. Then, we compared these patterns across teaching agents and their effects on learning outcomes. Overall, our study demonstrated that high-quality contingent mediations support childrens science vocabulary in learning regardless of the teaching agent.

IEEE RO-MAN 2024

Exploring Task-Level Contingent Mediations for Vocabulary Instruction across Robot, Virtual, and Human Teachers

therapy and rehabilitation; child-robot interaction; applications of social robots

robots in education

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

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Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Parental estrangement can cause long-term harm to socio-emotional skills, mental health and wellbeing. This is pronounced for children who experienced trauma. Robo- Therapists have been used to train children to express emotions, but such robots are often human replacements and tend to be too costly to deploy widely. We propose e-MoBo, a low-cost, non-humanoid Robo-Mediator of our own design to train childrens socio-emotional skills towards creating stronger relationships with others. We report on the developing e-MoBo and results of a field study with the current prototype and 14 neurotypical children, ages 5-9, as we prepare to deploy e-MoBo in a residential center with traumatized children. Our results suggest that e-MoBo, compared to a control, helps children express emotions, offering the promise of a low-cost, nonanthropomorphic, Robo-Mediator meeting childrens needs.

E-MoBo, a Low-Cost, “Robo-Mediator” Helping Therapists Teach Children How to Express Emotions Insights from Field Testing

Robots powered by Artificial Intelligence require continuous sensing to function and interact autonomously with their environment. This may create concerns for their users. Here, we describe a study in the context of an office environment equipped with autonomous delivery robots. Our study aims to understand the employees' current comprehension of the data captured by the robots, the nature of their concerns and ways to mitigate those. We found that the employees have little knowledge about data capture and that providing such knowledge can go either way, reassuring or generating new concerns which are often contextual. We qualitatively analysed reasons for having or not having concerns. Our findings include that trust in the employer is an important factor limiting them.

Investigating Privacy in the Context of Office Delivery Robots

Despite advances in areas such as the personalization of robots, sustaining adoption of robots for long-term use in families remains a challenge. Recent studies have identified integrating robots into families' routines and rituals as a promising approach to support long-term adoption. However, few studies explored the integration of robots into family routines and there is a gap in systematic measures to capture family preferences for robot integration. Building upon existing routine inventories, we developed Family-Robot Routines Inventory (FRRI), with 24 family routines and 24 child routine items, to capture parents' attitudes toward and expectations from the integration of robotic technology into their family routines. Using this inventory, we collected data from 150 parents through an online survey. Our analysis indicates that parents had varying perceptions for the utility of integrating robots into their routines. For example, parents found robot integration to be more helpful in children's individual routines, than to the collective routines of their families. We discuss the design implications of these preliminary findings, and how they may serve as a first step toward understanding the diverse challenges and demands of designing and integrating household robots for families.

Robots in Family Routines: Development of and Initial Insights from the Family-Robot Routines Inventory

A user-centered method of parameterizing robotic system is indispensable to enable workers with lower qualification levels the access to robotic systems. In this paper, different ways and types of communication are investigated to increase the usability during the programming of a movable robotic cell utilized for mounting processes inside a production line. Thereto, the entire usability circle given by the ISO 9241-210 is applied. Using the feedback of 13 participants from an initial formative evaluation inside a real production line, usability problems and six requirement categories were identified. Applying the prototypical design, we evaluated four possibilities of multimodal and bidirectional communication utilizing audio, haptic and visual channels. The results from the final usability study in the laboratory reveal that haptic plus visual input combined with visual plus audio feedback is assessed as most intuitive. Pre-experience with robots showed no significant influence on usability, acceptance and perceived safety in most modalities.

Investigating Human-Robot Interaction: A User-Centered Approach to Robotic System Setup and Programming

The field of robotics has shown significant advances in autonomous systems, particularly in robot navigation. Since the decisions made during navigation can be difficult for human operators to understand, research aims to provide explanations that improve human-robot interaction (HRI). However, generating and designing such explanations with the intention of improving robot-related human factors is still an ongoing research challenge. This paper addresses this challenge by investigating the impact of different explanation types on a set of human factors in the context of robot navigation. For this purpose, we conducted a user study that examined the impact of six different explanation types on commonly used human factors, including trust, satisfaction, situation awareness, likeability, understandability, and perceived usefulness. Additionally, the study provides indications of their general applicability for robot navigation explanations through creation of sum ranks across the observed human factor metrics. The results show that depending on the chosen explanation type, a significant impact on the measured factors can be observed. While constraint-based explanations are generally rated highly across all factors, apologetic explanations are not perceived well across all measured human factors. Our results provide insights into the impact of explanation types used for robot navigation scenarios on robot-related human factors, and also provide practical insights for designing explanations for robot navigation scenarios.

Influence of Different Explanation Types on Robot-Related Human Factors in Robot Navigation Tasks

This work illustrates the testing of the Spot Robot performed at the training camp of Civil Protection and ANPAS (National Association of Public Assistance) in Foligno. The camp simulates the aftermath of an earthquake with different types of collapsed buildings. We teleoperated the quadruped Spot robot in different areas of the camp where Spot needs to address different challenges. The focus of the testing was not on the objective performance of the robot but on how the robot was subjectively perceived by rescuers of ANPAS and Civil Protection. Initially, we formulated and tested two hypotheses to check if locomotion in some areas is perceived better than in other areas and if there are perceivable differences when the robot is using different types of locomotion gaits. Then, we conducted unstructured interviews with participants who observed the robot in action to describe their rescue procedures and give us suggestions and opinions on what operations they expect the robot might perform.

Perceptions and Opinions of Rescuers about a Quadruped Robot in an Earthquake Scenario

In the pursuit of enhancing the quality of life, research in domestic robotics is rapidly evolving, offering a broad spectrum of services that not only provide practical aid but also contribute to emotional well-being and social interaction. However, existing domestic robots often lack essential features necessary for continuous interaction with humans and their surroundings, such as stability, mobility, safety, size, and physical interaction capabilities. Addressing this need, this paper introduces the development of a prototype robot tailored for harmonious coexistence with humans. This robot utilizes its own heavy equipment, such as batteries and actuators, to achieve an intrinsically balanced structure along with a comprehensive set of features optimized for seamless integration into human-centric environments. Focusing on the mechanical design of the robot, we evaluated its performance through a series of experiments, highlighting its mechanical advantages, addressing its limitations, and proposing solutions to enhance its performance for future applications.

A Study on Designing a Robot with Body Features Tailored for Coexistence with Humans in Daily Life Environments

Recently, electroencephalographic (EEG) signals have been used to design and enhance human-robot interaction (HRI). In particular, error-related potentials (ErrPs) have been leveraged since very few years. These potentials can be used to provide feedback to the robot about any mismatch between the users expectations and the robots behavior, during interaction tasks. In this process, the correct classification of ErrPs is crucial, which, in turn, relies on the reliability of the process for extraction and selection of signal features. In this work, we consider an extensive list of possible features and perform a statistical analysis to assess their discriminative power for ErrP analysis. The aim is to reduce the number of features used for classification while retaining the most relevant ones only. Overall, the outcome of our study shows that some parameters have relevant importance compared to others, (i.e. temporal features, frequency features, signal processing features, and some wavelet transform coefficients), while some of the features used in existing works are not useful since they have low discriminative power.

Exploring the Most Significant Features for EEG ErrP Detection through Statistical Analysis

This article investigates mixed reality (MR) to enhance human-robot collaboration (HRC). The proposed solution adopts MR as a communication layer to convey a mobile manipulator's intentions and upcoming actions to the humans with whom it interacts, thus improving their collaboration. A user study involving 20 volunteers demonstrated the effectiveness of this MR-focused approach in facilitating collaborative tasks, with a positive effect on overall collaboration performances and human satisfaction.

Investigating Mixed Reality for Communication between Humans and Mobile Manipulators

The growing implementation of robots in societal contexts necessitates a deeper exploration of the dynamics of trust between humans and robots. This exploration should expand beyond traditional viewpoints that primarily emphasize the influence of robot performance. In the burgeoning area of social robotics, fine-tuning a robot's personality traits is increasingly recognized as a crucial element in shaping users' experiences during human-robot interaction (HRI). Research in this field has led to the creation of trust scales that encompass various trust dimensions in HRI. These scales include aspects related to performance as well as moral dimensions. Our previous study revealed that breaches of moral trust by robots impact human trust more negatively than performance trust breaches, and humans take retaliatory approaches in response to morality breaches by robots. In the present study, our main aim was to explore if trust loss and retaliation tendencies differ based on the identity of the teammates following the violations of these different trust aspects. Through multiple versions of an online search task, we examined our research questions and found that breaches of morality by robotic teammates cause a significantly higher trust loss in humans compared to human teammates. These findings highlight the importance of a robot's morality in determining how humans view a robot's trustworthiness. For effective robot design, robots must meet ethical and moral standards, which are higher than the ethical and moral standards expected from humans.

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E-MoBo, a Low-Cost, “Robo-Mediator” Helping Therapists Teach Children How to Express Emotions Insights from Field Testing

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