When communicating over long distances via telerobotic platforms, the ability to perform semantic haptic interactions is often lacking, stifling feelings of presence for those involved. One such interaction is handshaking—a haptic gesture used in various social contexts. This paper presents a within-subjects user study ( \(N=18\) ), where participants felt 14 unimodal actuated profiles and 1 no haptic (control) profile. The actuation of these profiles was facilitated by the Handshaking Over Long Distances (HOLD) system which uses radial squeeze, skin-stretch via a rocker, and vibration to simulate a handshake in the context of a telerobotic exchange. Participants’ impressions of the profiles were assessed via questionnaires. The findings from this study suggest that the type of haptic feedback (lateral skin stretch, radial squeeze, or vibration) has a larger effect on user preference and presence compared to the differences in profiles within each feedback type. All three feedback types provided a more preferable handshaking experience for the pilot user and increased presence compared to the control. However, radial squeeze was the most effective. This work highlights the importance of haptics, especially radial squeeze, in the remote handshaking experience and exhibits wearable haptic devices’ potential to improve the effectiveness of remote communication tools.

Mobile Remote Presence (MRP) robots have emerged out of the need for telepresence in various settings such as the workplace and hospitals. As with face-to-face experiences, these robot mediated encounters have social aspects that current commercially available MRP robots lack the capabilities to incorporate. In previous work, we integrated a manipulator onto a commercial telerobotic platform to enable expressive gestures and demonstrated that the gesturing capabilities enhanced the social connection between remote and local users. However, we also found that controlling the robot for complex interactions, such as a handshake, diminishes the remote user” s social experience. This paper presents the discovery of models for handshakes in different social contexts, which can be used in a shared-control architecture to reduce the effort on the remote user. Using a haptic measurement glove, force and inertia data was collected for human-human handshakes in various social contexts. By applying a k-nearest neighbor algorithm in combination with dynamic time warping and a support vector machine algorithm, two classification models are derived that predict the social context and can be used in an intelligent shared-control robot architecture.

Despite their technical advancements, commercially available telerobots are limited in social interaction capabilities for both pilot and local users, specifically in nonverbal communication. Our group hypothesizes that the introduction of expressive gesturing and tangible interaction capabilities (e.g., handshakes, fist bumps) will enhance telerobotic interactions and increase social connection between users. To investigate the affordances to social connection that gestures and tangible interactions provide in telerobot-mediated interactions, we designed and integrated a lightweight manipulator terminating in an anthropomorphic end effector onto a commercially available telerobot (Anybots QB 2.0). Through virtual reality tracking of the pilot user’s arm and hand, expressive gestures and social contact interactions are recreated via the manipulator, enabling a pilot user and a local user to engage in a tangible exchange. To assess the usability and effectiveness of the gesturing system, we present evaluations from both the local and pilot user perspectives. First, we present a validation study to assess usability of the control system by the pilot user. Our results demonstrate that pilot user interactions can be replicated with a greater than 80% pass rate and mean ease of use rating of \(7.08 \pm 1.32\) (out of 10) with brief training. Finally, we present a user study to assess the social impacts of (1) using the telerobot without the manipulator from both the pilot user and local user perspectives and (2) using the control system and telerobotic manipulator from both the pilot user and local user perspectives. Results demonstrate that the robot with the manipulator elicited a more positive social experience than the robot without the arm for local users but no significant difference in conditions for pilot users. Future work will focus on improving the pilot user experience to support social contact interactions.

Although research indicates that telepresence robots offer a more socially telepresent alternative to conventional forms of remote communication, the lack of touch-based interactions presents challenges for both remote and local users. In order to address these challenges, we have designed and implemented a robotic manipulator emulating a human arm. However, contact interactions like handshakes with a robotic manipulator may feel awkward and unnatural to local users. In this work, we present the design of a wearable haptic measurement glove (HMG) and use it to collect force and inertial data on handshakes in human-human and humanrobot interactions in the interest of developing intelligent shared control algorithms for natural, human-like contact interactions in human-robot interactions.

Recent research on telepresence robots demonstrates that while they enable new heights of remote communication, there still exists challenges for both local and remote users in creating a connectedness one only encounters in face-to-face interactions. A large part of communication is beyond hearing and vision. Tangible interactions, expressive gestures, and physical referencing represent three of the primary social behaviors missing in the current telepresence experience. There is an inherent, subconscious quality to these physical actions that has been shown to allow more expressive and engaging communication. In this project we present the design, fabrication, and initial performance validation of a lightweight, ergonomic manipulator with a heavy, anthropomorphic end effector that enables gesturing capabilities for telepresence interactions.

This paper describes the findings of a research study of a blended-learning approach to train mentors of teams in the Botball® Educational Robotics Program. Botball is an international team-based robotics competition for secondary students designed to build skills in computer programming, robotics, teamwork, and problem solving. For this study, we recruited new teams comprising 8-10 middle school students per team and a mentor. Teams were randomly assigned to one of three treatment groups or a control group. Mentors of teams in the experimental groups received training in one of three types of mentor practices: best practices, mentoring for self-efficacy, or a combination of best practices and self-efficacy. The training format consisted of web-based self-paced tutorials, a face-to-face workshop, and webinars. Dependent variables were student post-test scores on three assessments: Efficacy for Science-Related Jobs, STEM Achievement-Related Choices, and STEM Self-Efficacy. A priori statistical analyses showed no difference between the groups; however, post hoc analyses showed that the use of self-efficacy techniques was positively related to the three dependent measures. Post-competition surveys of mentor practices indicated that students in the treatment groups did not appear to receive distinctly different treatments, revealing some of the potential challenges of the blended learning approach for professional development of teachermentors.

As a robot formation increases in size or explores places where it is difficult for a human operator to interact, autonomous control becomes critical. We propose a distributed autonomous method for evaluating properties of multi-robot systems, and then discuss how this information can be applied to improve performance with respect to a given operation. We present this as an extension of our previous work on robot formations; however, the techniques described could be adapted to other multi-robot systems.

The field of multi-robot coordination, specifically robot formation control, is rapidly expanding, with many applications proposed. In our previous work, we considered the problem of establishing and maintaining a formation of robots given an already connected network. We now propose a distributed auction-based method to autonomously initialize and reorganize the network structure of a formation of robots.

The goal of a robot formation control architecture is to get a number of robots into a specified form. To be effective and practical, the control architecture must be able to transition a group of robots from an initial swarm to a final formation. It must then be able to handle real-world events that could disrupt the formation, thus, requiring formation repair, obstacle avoidance, and changes in the formation. In previous work, we presented a distributed, reactive cellular automata-based formation control architecture capable of controlling any number of robots in formation at once. In this paper, we examine our architecture with respect to necessary characteristics to handle real-world occurrences. To address issues of formation repair and obstacle avoidance, the control architecture is extended by a distributed auctioning method that allows the robot formation to reconfigure autonomously.

In this paper, we introduce the concept of breadth-last technical elective courses, which are designed to assist undergraduate CS students in integrating their entire core curriculum into a coherent whole at the end of their degree programs. Specific breadth-last courses in intelligent mobile robotics and computer game development have been implemented and are presented here to demonstrate the pedagogical concepts being discussed.

Real-world systems are comprised of interdependent components creating integrated systems. These systems are developed by multidisciplinary teams. The goal of this project is the development of a comprehensive undergraduate course in robotics that encompasses various fields that are integral to robotic systems: Computer Science, Electrical and Computer Engineering, and Mechanical Engineering. A main pedagogical goal of the course is to teach group dynamics and the skills necessary for interaction with people in different disciplines in multidisciplinary teams. Descriptions of the course and the hands-on lab assignments are presented along with course assessment.

Empirical evidence shows the ability for computer technology to deliver on its promises of enhancing our quality of life relies on how well the application fits our understanding of how things work. Software designers need to apply methods that provide insights into the user's mental model of the application's target task and to invite the user to be an active participant in the design process. This paper reports on our efforts to design an HCI curriculum around ethnographic techniques of data gathering and paper prototyping. Initial results are presented that study the course's effects on student's attitudes regarding approaches to software design and their long term design behavior.

The data exploration task can be divided into three interrelated subtasks: 1) feature selection, 2) discovery, and 3) interpretation. This paper describes an unsupervised discovery method with biases geared toward partitioning objects into clusters that improve interpretability. The algorithm ITERATE employs: 1) a data ordering scheme and 2) an iterative redistribution operator to produce maximally cohesive and distinct clusters. Cohesion or intraclass similarity is measured in terms of the match between individual objects and their assigned cluster prototype. Distinctness or interclass dissimilarity is measured by an average of the variance of the distribution match between clusters. The authors demonstrate that interpretability, from a problem-solving viewpoint, is addressed by the intraclass and interclass measures. Empirical results demonstrate the properties of the discovery algorithm and its applications to problem solving.

Traditional reasoning methods have found limited success in the medical domain due to their brittleness and lack of robust justification. Recent work has turned to model-based reasoning techniques to overcome these limitations. These techniques employ deep domain models and focus on qualitative reasoning. A discussion is presented of an ongoing project to develop a cardiovascular model with sophisticated reasoning mechanisms and robust explanation capabilities. The framework for the model combines J. DeKleer and J.S. Brown's (1984) component-oriented methods and K.D. Forbus's (1984) process-oriented ontologies. The current prototype models the electrical subsystem of the heart, and simulates normal and deviant cardiac rhythms.>