Mixed human-robot teams (HRTs) have the potential to perform complex tasks by leveraging diverse and complementary capabilities within the team. However, assigning humans to operator roles in HRTs is challenging due to the significant variation in user capabilities. While much of prior work in role assignment treats humans as interchangeable (either generally or within a category), we investigate the utility of personalized models of operator capabilities based in relevant human factors in an effort to improve overall team performance. We call this approach individualized role assignment (IRA) and provide a formal definition. A key challenge for IRA is associated with the fact that factors that affect human performance are not static (e.g., one's ability to track multiple objects can change during or between tasks). Instead of relying on time-consuming and highly-intrusive measurements taken during the execution of tasks, we propose the use of short cognitive tests, taken before engaging in human-robot tasks, and predictive models of individual performance to perform IRA. Results from a comprehensive user study conclusively demonstrate that IRA leads to significantly better team performance than a baseline method that assumes human operators are interchangeable, even when we control for the influence of the robots' performance. Further, our results point to the possibility that such relative benefits of IRA will increase as the number of operators (i.e., choices) increase for a fixed number of tasks.

The Robot Olympics: Estimating and Influencing Beliefs about a Robot's Perceptual Capabilities

With becoming an aging society rapidly, the digital divide among the elderly is expected to emerge as a social problem. In order to bridge the digital gap between the elderly and the general public, it is necessary to find out what difficulties the elderly have in using information and communication technology (ICT) and what technologies and services they need. In order to figure out those issues, we conducted three phases of study: ICT literacy survey, interview, and design workshop (N=10). This study has three major findings. First, from the physical view, the elderly found ICT that reduces their movement distance attractive. Second, from the cognitive perspective, services which reduce the gap between the real physical ability of the elderly and the perceived ability of them by themselves are needed. Lastly, from the psychological view, an intuitive and natural interface is needed for the elderly who are afraid of learning new technology.

Exploring ICT for the Elderly: By Analyzing the Elders’ Needs for Information Acquisition and Delivery

Social robots have been shown effective in pedagogical settings due to their embodiment and social behavior that can improve a learner’s motivation and engagement. In this study, the impact of a social robot’s motivational gestures in robot-assisted language learning (RALL) was investigated. Twenty-five university students participated in a language learning task tutored by a NAO robot under two conditions (within-subjects design); in one condition the robot provided positive and negative feedback on participant’s performance using both verbal and non-verbal behavior (Gesture condition), in another condition the robot only employed verbal feedback (No-Gesture condition). To assess cognitive engagement and learning in each condition, we collected EEG brain activity from the participants during the interaction and evaluated their word knowledge during an immediate and delayed post-test. No significant difference was found with respect to cognitive engagement as quantified by the EEG Engagement Index during the practice phase. Similarly, the word test results indicated an overall high performance in both conditions, suggesting similar learning gain regardless of the robot’s gestures. These findings do not provide evidence in favor of robot’s motivational gestures during language learning tasks but at the same time indicate challenges with respect to the design of effective social behavior for pedagogical robots.

Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

Laboratories are being increasingly automated. In
small laboratories individual processes can be fully automated,
but this is usually not economically viable. Nevertheless, in-
dividual process steps can be performed by flexible, mobile
robots to relieve the laboratory staff. As a contribution to the
requirements in a life science laboratory the mobile, dextrous
robot Kevin was designed by the Fraunhofer IPA research
institute in Stuttgart, Germany. Kevin is a mobile service robot
which is able to fulfill non-value adding activities such as
transportation of labware. This paper gives an overview of
Kevin’s functionalities, its development process, and presents a
preliminary study on how its lights and sounds improve user
interaction.

 SM-EXO: Shape Memory alloy-based Hand EXOskeleton for Cobotic Application in Cognitive Development Investigation

We are approaching a future where social robots will progressively become widespread in many aspects of our daily lives, including education, healthcare, work, and personal use. All such practical applications require that humans and robots collaborate in human environments, where social interaction is unavoidable. Along with verbal communication, successful social interaction is closely coupled with nonverbal communication. Humans perform nonverbal communication in an instinctive and adaptive way, with no effort. For robots to be successful in our social landscape, they should therefore engage in social interactions in a human-like manner, with increasing levels of autonomy. Understanding human-human interaction is therefore the most natural guide to designing human-robot interaction interfaces that can be usable and understandable by everyone. The key aim of this special session is to bring forth efforts to understand mechanisms underlying human-human nonverbal communication and introduce novel approaches to the design, development, and evaluation of robotic platforms inspired and driven by those mechanisms. This special session will bring together researchers with diverse backgrounds, ranging from social psychology, cognitive science, machine learning, computer vision, and human-robot interaction, to share innovative ideas, findings, and challenges related to nonverbal communication skills in humans and robots. The special session will foster multidisciplinary discussions among academic and industrial researchers towards improving robots’ nonverbal communication skills and enhancing human trust and acceptance of robots.

Service Social Robotics from industry point of view

In order to test how explanations affect a user working together with a collaborative robot, we created a test scenario where a user sorts trash together with a collaborative robot. Sometimes the robot is not able to fulfill its part of the task. Different modalities (textual, graphical, both) for explaining this error to the user are tested in a between subjects design and the effects on task performance, cognitive load and trust are analyzed.

Giving Social Robots a Conversational Memory for Motivational Experience Sharing

Using robot manipulators in contexts where it is undesirable or impractical for humans to physically intervene is crucial for several applications, from manufacturing to extreme environments. However, robots require a high degree of intelligence to operate in those environments, especially if they are not fully structured. 
Teleoperation compensates for this limitation by connecting the human operator to the robot using human-robot interfaces. The remotely operated sessions can also be used as demonstrations to program more powerful autonomous agents.
In this article, we report a thorough user study to characterise the effect of simple vibrotactile feedback on the performance and cognitive load of the human user in performing teleoperated. grasping and manipulation tasks. The experiments are performed using a portable and affordable bilateral teleoperation system that we designed, composed of a Leap Motion sensor and a custom-designed vibrotactile haptic glove to operate a 4-fingered robot hand equipped with 3-axis force sensors on the fingertips; the software packages we developed are open-source and
publicly available. Our results show that vibrotactile feedback improves teleoperation and reduces cognitive load, especially for complex in-hand manipulation tasks.

Creating Informative and Successful Human Robot Interaction in a Real-World Service Environment

The rapid spread of COVID-19 around the globe has increased the need to adopt autonomous social robots within our healthcare systems. In particular, socially assistive robots can help to improve the day-to-day functioning of our healthcare facilities including long-term care, while keeping residents and staff safe by performing repetitive tasks such as health screening. In this paper, we present the first human-robot interaction study with an autonomous multi-task socially assistive robot used for non-contact screening in long-term care homes. The robot monitors temperature, checks for face masks, and asks screening questions to minimize human-to-human contact. We investigated staff perceptions of 7 attributes: screening experience without and with the robot, efficiency, cognitive attitude, freeing up staff, safety, affective attitude, and intent to use the robot. Furthermore, we investigated the influence of demographics on these attributes. Study results show that, overall, staff rated these attributes high for the screening robot, with a statistically significant increase in cognitive attitude and safety after interacting with the robot. Differences between gender and occupation were also determined. Our study highlights the potential application of an autonomous screening robot for long-term care homes.

Tolerating Untrustworthy Robots: Studying Human Vulnerability Experience within a Privacy Scenario for Trust in Robots

Human gaits constitute significant health information because they indicate body function conditions, such as sensory, motor, and cognitive functions. Monitoring gait patterns helps to find weakened body parts and to prevent diseases before they become serious. In this paper, we propose a novel method to monitor human gaits in indoor environments by using a mobile robot and artificial intelligence. A mobile robot periodically collects 3D skeleton data by using an installed depth camera, and the collected data are used to extract gait parameters and to classify normal, antalgic, stiff-legged, steppage, lurching, and Trendelenburg gaits via the proposed system. Ultrawideband (UWB)-based localization with Kalman filtering and odometry-based state estimation is adopted to allow a mobile robot to rapidly and accurately reach the target human. We use both 3D skeletons and joint-based angle data to achieve improved pathological gait classification performance. A bidirectional gated recurrent unit (Bi-GRU) architecture is adopted for fusion at the feature level. An accuracy of 97.64% is achieved by using the proposed multiple-input model, whereas 96.63% and 94.17% accuracies are achieved by the skeleton- and joint-based angle input models, respectively. The proposed automatic gait analysis method can contribute to improving untact smart home care and quality of life.

Domestic Social Robots as Companions or Assistants? The Effects of the Robot Positioning on the Consumer Purchase Intentions

In this paper, we introduce Ommie, a novel robot that supports deep breathing practices for the purposes of anxiety reduction. The robot’s primary function is to guide users through a series of extended inhales, exhales, and holds by way of haptic interactions and audio cues. We present core design decisions during development, such as robot morphology and tactility, as well as the results of a usability study in collaboration with a local wellness center. Interacting with Ommie resulted in a significant reduction in STAI-6 anxiety measures, and participants found the robot intuitive, approachable, and engaging. Participants also reported feelings of focus and companionship when using the robot, often elicited by the haptic interaction. These results show promise in the robot’s capacity for supporting mental health.

Leveraging Cognitive States in Human-Robot Teaming

Cognitive training is effective at retaining cognitive function and delaying decline for typically ageing older adults, individuals with mild cognitive impairment, and persons with dementia. Technological resources can address limiting factors that inhibit engagement and access to this treatment. We investigated how a socially assistive robot-facilitated memory task with sensory feedback was received by older adults. The impact of unimodal and multimodal administration of auditory and haptic feedback using two robot embodiments (Pepper and Nao) was evaluated in terms of user performance, usability, and workload. In contrast to sensory feedback research, auditory feedback resulted in significantly higher task accuracy. This was, however, supported by previous work from neurological literature. Auditory feedback also received significantly higher usability, and this preference was validated by qualitative feedback from participants. Regardless of robotic embodiment, this study demonstrates an advantage for auditory feedback (over haptic and multimodal) in cognitive training activities for older adults.

Investigating the Usability of a Socially Assistive Robotic Cognitive Training Task with Augmented Sensory Feedback Modalities for Older Adults

Individuals living with cognitive impairments are faced with unique challenges in completing important activities of daily living such as dressing. In this paper, we present the first socially assistive robot-wearable sensors system to provide dressing assistance through social human-robot interactions. A novel robot-wearable architecture is development to classify, prompt and provide feedback on user dressing actions. Namely, strain sensor based smart clothing on the user are used for joint angle mapping, which are then classified into different dressing steps. The robot uses a MAXQ hierarchical learning method to learn assistive behaviors to aid a user with the sequence of dressing steps. Experiments were validated the performance of the joint angle mapping model, dressing action classifier, and behavior adaptation modules as well as the overall system for dressing assistance.

Underline digital video library

The Robot Olympics: Estimating and Influencing Beliefs about a Robot's Perceptual Capabilities

Exploring ICT for the Elderly: By Analyzing the Elders’ Needs for Information Acquisition and Delivery

Motivational Gestures in Robot-Assisted Language Learning: A Study of Cognitive Engagement using EEG Brain Activity

SM-EXO: Shape Memory alloy-based Hand EXOskeleton for Cobotic Application in Cognitive Development Investigation

Service Social Robotics from industry point of view

Giving Social Robots a Conversational Memory for Motivational Experience Sharing

Creating Informative and Successful Human Robot Interaction in a Real-World Service Environment

Tolerating Untrustworthy Robots: Studying Human Vulnerability Experience within a Privacy Scenario for Trust in Robots

Domestic Social Robots as Companions or Assistants? The Effects of the Robot Positioning on the Consumer Purchase Intentions

Leveraging Cognitive States in Human-Robot Teaming

Investigating the Usability of a Socially Assistive Robotic Cognitive Training Task with Augmented Sensory Feedback Modalities for Older Adults

Self-Disclosure to a Robot “In-the-Wild”: Category, Human Personality and Robot Identity

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