The environments in which the collaboration of a robot would be the most helpful to a person are frequently uncontrolled and cluttered with many objects present. Legible robot arm motion is crucial in tasks like these in order to avoid possible collisions, improve the workflow and help ensure the safety of the person. Prior work in this area, however, focuses on solutions that are tested only in uncluttered environments and there are not many results taken from cluttered environments. In this research we present a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Both our measure and the planner were tested in a cluttered environment meant to represent a more typical tool-sorting task for which the person would collaborate with a robot. The in-person validation study with Baxter robots shows a significant improvement in legibility of our proposed legible motion planner compared to the current state-of-the-art legible motion planner in cluttered environments. Further, the results show a significant difference in the performance of the planners in cluttered and uncluttered environments, and the need to further explore legible motion in cluttered environments. We argue that the inconsistency of our results in cluttered environments with those obtained from uncluttered environments points out several important issues with the current research performed in the area of legible motion planners.

Facial expressiveness plays a crucial role in a robot’s ability to engage and interact with children. Prior research has shown that expressive robots can enhance child engagement during human-robot interactions. However, many robots used in therapy settings feature non-personalized, static faces designed with traditional facial feature considerations, which can limit the depth of interactions and emotional connections. Digital faces offer opportunities for personalization, yet the current landscape of robot face design lacks a dynamic, user-centered approach. Specifically, there is a significant research gap in designing robot faces based on child preferences. Instead, most robots in child-focused therapy spaces are developed from an adult-centric perspective. We present a novel study investigating the influence of child-drawn digital faces in child-robot interactions. This approach focuses on a design activity with children instructed to draw their own custom robot faces. We compare the perceptions of social intelligence (PSI) of two implementations: a generic digital face and a robot face, personalized using the user’s drawn robot faces. The results of this study show the perceived social intelligence of a child-drawn robot was significantly higher compared to a generic face.

This innovative practice WIP paper describes our ongoing development and deployment of an online robotics education platform that highlighted a gap in providing an interactive, feedback-rich learning environment essential for mastering pro-gramming concepts in robotics, which they were not getting with the traditional code→ simulate→turn-in workflow. Since teaching resources are limited, students would benefit from feedback in real-time to find and fix their mistakes in the programming assignments. To integrate such automated feedback, this paper will focus on creating a system for unit testing while integrating it into the course workflow. We facilitate this real-time feedback by including unit testing in the design of programming assignments so students can understand and fix their errors on their own and without the prior help of instructors/TAs serving as a bottleneck. In line with the framework's personalized student-centered approach, this method makes it easier for students to revise and debug their programming work, encouraging hands-on learning. The updated course workflow, which includes unit tests, will strengthen the learning environment and make it more interactive so that students can learn how to program robots in a self-guided fashion.

In human-robot collaboration, legible intent of the robot is critical to success as it enables the human to more effectively work with and around the robot. Environments where humans and robots collaborate are widely varied and in the real world are most often cluttered. However, prior work in legible motion utilizes primarily environments which are uncluttered. Success in these environments does not necessarily guarantee success in more cluttered environments. Furthermore, the prior work has been primarily performed based on results from robot-human studies and the problem has not been studied from the prospective of what people do to express intent to each other. Therefore, this work addresses a gap in current research into legible robot arm motion in the following ways: first we perform a human-human study in order to establish the factors which humans use to express their intent through body language, and second we perform the study in a cluttered and varied environment. Through the study we showed that the primary factors which people considered are: timing, kinematic parameters, hand gestures, object proximity, etc. The results also showed that legibility is correlated with perceived safety, perceived social intelligence, the collaborator’s contribution, and trust which further speaks to the importance of legible motion. Future work will utilize the pose data extracted from the study’s video recordings to develop a model for legible motion.

Research demonstrates a growing mental health crisis in graduate education, which can contribute to productivity, departure, and well-being issues. To address this crisis and advocate for systemic change, this project explored faculty perceptions about graduate student mental health and how these perceptions intersect with direct action when student mental health challenges arise. We were guided by phenomenological inquiry to explore how faculty attitudes (n = 3) about mental health shape programmatic and individual decisions around supporting mental health. We thematically analyzed interviews discussing stress and mental health focused on faculty experiences. Faculty interviews demonstrated varying attitudes toward graduate student stress and mental health. Faculty desires to engage in discussions about stress or mental health were on a wide spectrum, often with work productivity guiding these discussions. Further, faculty highlighted levels of discomfort with engaging in discussions about mental health, especially with the students they work closest with. Findings indicate a need to foster faculty skill and comfort with engaging with students about their mental health while also providing clear institutional policies that support these actions to address the mental health crisis.

The research paper examines how engineering doctoral students describe their awareness and experiences with stress and mental health during their graduate studies. Despite the known bidirectional relationship between stress and mental health, there is limited research on how engineering doctoral students rationalize the disparity between the health consequences of chronic stress and the veneration of academic endurance in the face of these challenges. Given the dangers of chronic stress to physical and mental health, it is important to understand how students perceive the purpose and impact of stress and mental health within overlapping cultures of normalized stress. We conducted semi-structured interviews to understand participants' awareness, conceptualizations, and interpretations of stress and mental health. The research team analyzed interview transcripts using content analysis with inductive coding. Overall, we found that our participants recognized behavioral changes as an early sign of chronic stress while physical changes were a sign of sustained chronic stress; these cues signaled that participants needed additional support, including social support and campus mental health services. These findings support the need for greater mental health awareness and education within engineering doctoral programs to help students identify and manage chronic stress.

This paper presents a work-in-progress on a learning system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skilled robotics instructors, particularly in community colleges and the expensive demand for training equipment. The study of robotics at the college level represents a wide range of interests, experiences, and aims. This project works to provide students the flexibility to adapt their learning to their own goals and prior experience. We are developing a system to enable robotics instruction through a web-based interface that is compatible with less expensive hardware. Therefore, the free distribution of teaching materials will empower educators. This project has the potential to increase the number of robotics courses offered at both two- and four-year schools and universities. The course materials are being designed with small units and a hierarchical dependency tree in mind; students will be able to customize their course of study based on the robotics skills they have already mastered. We present an evaluation of a five module mini-course in robotics. Students indicated that they had a positive experience with the online content. They also scored the experience highly on relatedness, mastery, and autonomy perspectives, demonstrating strong motivation potential for this approach.

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying “what to do” in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make “tea” and “sandwich”. We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says “I am thirsty” or “It is cold outside” the robot will start to perform the tea-making skill. In contrast, if the person says, “I am hungry” or “I need something to eat”, the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill.

Autonomous vehicles have an existential communication challenge due to the lack of need for a human driver who can signal to vulnerable road users nearby about the intentions of the vehicle. This presents an opportunity for a vehicle to vulnerable road user communication system, such as for bicyclists. Enabling communication between bicyclists and autonomous vehicles will lead to an improvement of the bicyclists’ safety in autonomous driving. If a bicyclist wants to pass the autonomous vehicle, the autonomous vehicle should provide feedback to the human about what it is about to do and what it would like the person to do. The user study presented in this paper investigated several possible options for an external display for effective nonverbal communication between an autonomous vehicle and a bicyclist. The results were compared to our recent study concerning vehicle-to-pedestrian communication. In total 208 participants were recruited for the vehicle-to-walker and vehicle-to-bicyclist feedback module studies. The results did not show significant differences between the communication modalities presented. This paper shows and discusses differences between vehicle-to-walker and vehicle-to-bicyclist feedback modules. It is plausible to use the same combination of interaction modes, symbols and text, as for the vehicle-to-pedestrian communication feedback module due to economic reasons. This study shows the necessity for more immersive environments to study vehicle to bicyclist communication needs in more detail.

Mobile robots must navigate efficiently, reliably, and appropriately around people when acting in shared social environments. For robots to be accepted in such environments, we explore robot navigation for the social contexts of each setting. Navigating through dynamic environments solely considering a collision-free path has long been solved. In human-robot environments, the challenge is no longer about efficiently navigating from one point to another. Autonomously detecting the context and adapting to an appropriate social navigation strategy is vital for social robots’ long-term applicability in dense human environments. As complex social environments, museums are suitable for studying such behavior as they have many different navigation contexts in a small space.Our prior Socially-Aware Navigation model considered con-text classification, object detection, and pre-defined rules to define navigation behavior in more specific contexts, such as a hallway or queue. This work uses environmental context, object information, and more realistic interaction rules for complex social spaces. In the first part of the project, we convert real-world interactions into algorithmic rules for use in a robot’s navigation system. Moreover, we use context recognition, object detection, and scene data for context-appropriate rule selection. We introduce our methodology of studying social behaviors in complex contexts, different analyses of our text corpus for museums, and the presentation of extracted social norms. Finally, we demonstrate applying some of the rules in scenarios in the simulation environment.

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot could select and account for social objectives related to navigation autonomously.

The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized—and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be “business as usual,” with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that cannot be published or in work whose true impact is difficult to accurately assess. Yet HRI research performed in the coming years will be changed in fundamentally different ways; the inability to perform—or expect future performance of—in-person human subjects research, especially research involving tactile or multiparty interaction, will change both the dominant methodological techniques employed by HRI researchers and the very research questions that the field chooses to—and is able to—address. These challenges demand that HRI researchers identify precisely how the field can maintain research quality and impact while the ability to conduct human-subject studies is severely impaired for an undetermined amount of time. A natural inclination may be simply to wait the crisis out in the hope of a speedy return to normalcy; however, in this article, we argue that the community can also take this opportunity to reevaluate and refocus how research in this field is conducted and how students are mentored in ways that will yield benefits for years to come after the current crisis has ended.

Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System’s (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN).

Robotic social intelligence is increasingly important. However, measures of human social intelligence omit basic skills, and robot-specific scales do not focus on social intelligence. We combined human robot interaction concepts of beliefs, desires, and intentions with psychology concepts of behaviors, cognitions, and emotions to create 20 Perceived Social Intelligence (PSI) Scales to comprehensively measure perceptions of robots with a wide range of embodiments and behaviors. Participants rated humanoid and non-humanoid robots interacting with people in five videos. Each scale had one factor and high internal consistency, indicating each measures a coherent construct. Scales capturing perceived social information processing skills (appearing to recognize, adapt to, and predict behaviors, cognitions, and emotions) and scales capturing perceived skills for identifying people (appearing to identify humans, individuals, and groups) correlated strongly with social competence and constituted the Mind and Behavior factors. Social presentation scales (appearing friendly, caring, helpful, trustworthy, and not rude, conceited, or hostile) relate more to Social Response to Robots Scales and Godspeed Indices, form a separate factor, and predict positive feelings about robots and wanting social interaction with them. For a comprehensive measure, researchers can use all PSI 20 scales for free. Alternatively, they can select the most relevant scales for their projects.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using unmanned aerial vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper, we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

Nonverbal interactions are a key component of human communication. Since robots have become significant by trying to get close to human beings, it is important that they follow social rules governing the use of space. Prior research has conceptualized personal space as physical zones which are based on static distances. This work examined how preferred interaction distance can change given different interaction scenarios. We conducted a user study using three different robot heights. We also examined the difference in preferred interaction distance when a robot approaches a human and, conversely, when a human approaches a robot. Factors included in quantitative analysis are the participants' gender, robot's height, and method of approach. Subjective measures included human comfort and perceived safety. The results obtained through this study shows that robot height, participant gender and method of approach were significant factors influencing measured proxemic zones and accordingly participant comfort. Subjective data showed that experiment respondents regarded robots in a more favorable light following their participation in this study. Furthermore, the NAO was perceived most positively by respondents according to various metrics and the PR2 Tall, most negatively.

This study evaluated how a robot demonstrating a Theory of Mind (ToM) influenced human perception of social intelligence and animacy in a human-robot interaction. Data was gathered through an online survey where participants watched a video depicting a NAO robot either failing or passing the Sally-Anne false-belief task. Participants (N=60) were randomly assigned to either the Pass or Fail condition. A Perceived Social Intelligence Survey and the Perceived Intelligence and Animacy subsections of the Godspeed Questionnaire Series (GQS) were used as measures. The GQS was given before viewing the task to measure participant expectations, and again after to test changes in opinion. Our findings show that robots demonstrating ToM significantly increase perceived social intelligence, while robots demonstrating ToM deficiencies are perceived as less socially intelligent.

The impact of technology on workforce development and socioeconomic prosperity has made K-12 computing engineering and STEM in general a national educational priority. However, the integration of computing remains obstructed by resources and lack of professional development to support students' learning. Further challenging is students' STEM attitudes and interest do not matriculate with them into higher education. This issue is especially critical for traditionally under represented and underserved populations including females, racial/ethnic minority groups, and students of low-socioeconomic status (SES). To help mitigate challenges, we developed an unplugged (computer-less) computing engineering and robotics lesson composed of two introductory computing concepts, sequencing and decision-making, using a small robot arm and tangible programming blocks. Through students' sequencing of operations, debugging, and executing complex robotic behavior, we seek to determine if students' interest or attitudes change toward engineering. Nine one-hour introductory pilot lessons with 148 students, grades 6-10, at two public middle schools, and one summer camp were conducted. We measured students' engineering interest and attitudes through a 15 question pre- and post-lesson survey and calculated aggregate factor scores for interest and attitudes. We found low-SES students' a priori interests and attitudes tend to be lower and more varied than those of their high-SES peers. These preliminary results suggest that the integration of introductory computing and robotics lessons in low-SES classrooms may help students reach similar levels of engineering interest and attitudes as their high-SES peers.

For socially assistive robots (SAR)to be accepted into complex and stochastic human environments, it is important to account for subtle social norms. In this paper, we propose a novel approach to socially-aware navigation (SAN)which garnered an immense interest in the Human-Robot Interaction (HRI)community. We use a multi-objective optimization tool called the Pareto Concavity Elimination Transformation (PaC-cET)to capture the non-linear human navigation behavior, a novel contribution to the community. A candidate point on a trajectory is scored (1)for its progress towards the goal, and (2)based on autonomously-sensed distance-based features that capture the social norms and associated social costs. Rather than use a finely-tuned linear combination of these costs, we use PaCcET to select an optimized future trajectory point, associated with a non-linear combination of the costs. Existing research in this domain concentrates on geometric reasoning, model-based, and learning approaches, which have their own pros and cons. This approach is distinct from prior work in this area. We showed in a simulation that the PaCcET-based trajectory planner not only is able to avoid collisions and reach the intended destination in static and dynamic environments but also considers a human's personal space i.e. rules of proxemics in the trajectory selection process.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas it will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAV) to cover wildfire is promising because it can replace humans for fire tracking, reducing hazards and saving operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

In this paper, we revisit a real-time socially-aware navigation planner which helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This paper presents an evaluation done in simulation while utilizing data from real human interactions.

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this work, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from the Beta component of a user's EEG. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe ‘home’ location. This system has been validated on a commercial UAV under malicious attack conditions.

Dear colleagues in the HRI community, I have been given the honor and privilege to write the editorial introduction for this final issue of the Journal of Human-Robot Interaction, before its reemergence as the ACM Transactions on Human-Robot Interaction. It has been a distinct pleasure to serve as the JHRI Managing Editor for the last two and a half years. I am grateful to our founding editors, Sara Kiesler and Mike Goodrich, for giving me this opportunity. Working with our current editors, Chad Jenkins and Selma Sabanovic, we have made great steps to continue advancing the journal and its scholarship. All of us are already working hard to produce an outstanding first issue of ACM THRI!

Socially assistive robotics (SAR) has increasingly been shown to have potential as a tool for social skills therapy for children with autism, a developmental disorder associated with atypical social development. This work presents the results of a study of robot agency on child-robot interactions involving children with autism. We describe the development of a SAR interaction scenario with both agent-like and object-like robot behaviors and present the results of a pilot study of six children with autism interacting with a humanoid robot with the full controller, as well as three types of control: a non-humanoid box robot with similar behavior (reduced morphological agency), a humanoid robot with random behavior (reduced behavioral agency), and a robotic toy (reduced morphological and behavioral agency). We find that the children can be divided into two groups depending on their reaction to the robot; for some children, the robot was an engaging object that elicited social behavior by providing novel and appealing sensory experiences (primarily bubbleblowing), while for other children, the robot was an agent and elicited social behavior through agentlike actions such as autonomous movement. We found that the first group had small differences between robot conditions and vocalized most with the bubble-blowing toy, while the second group vocalized most with the humanoid robots and looked less at the humanoid portion of the robot with reduced behavioral agency.

People with visual impairments have a hard time getting consistent physical exercise, as they can not do some exercises, such as running outside, without a sighted guide. People with visual impairments have been shown to have higher spatial localization skills than sighted people, which lead us to believe that they could follow a drone on a running-track environment. This paper presents a feasibility study where we investigate the ability to localize and follow a low-cost flying drone in people with visual impairments. A Wizard of Oz style study was conducted with 2 blind participants. Our results indicate that blind individuals can accurately localize the drone and follow it. Qualitative results also indicate that the participants were comfortable with following the drone and had high efficacy when it came to following and localizing the drone. The study supports future development of a fully functioning prototype.

Just as abusive behavior can play define the nature of a human-human interaction, mistreatment can play a similar role in Human-Robot Interaction. Earlier work demonstrated that people perceived a robot as more emotionally capable than a computer. This led to different perceptions of aggressive behavior (as mistreatment for a robot, but not so for a computer). This study is a follow-up to that work studying how much the morphology of a robot is responsible for changes in perceived emotional capability. We collected data from 80 participants. Participants interacted with a robot and a confederate who either acted aggressively or neutrally towards the robot. We hypothesized that a large robot would not be perceived as emotionally capable as a small robot, and that the large robot would not be seen as mistreated. The participants showed no significant perception of mistreatment toward the large robot. Participants also felt the large robot was less emotionally capable. We found that when verbal abuse was directed at a larger robot, participants would not consider such behavior mistreatment, but they would when similar abuse was directed at a child-size robot.

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.

Sensing and interpreting the user's activities and social behavior, monitoring the dynamics of the social context, and selecting and producing appropriate robot action are the core challenges involved in using robots as social tools in interaction scenarios. In social human-robot interaction, speech and gesture are the commonly considered interaction modalities. In human-human interactions, interpersonal distance between people can contain significant social and communicative information. Thus, if human-robot interaction reflects this human-human interaction property, then human-robot distances also convey social information. If a robot is to be an effective social agent, its actions, including those relating to interpersonal distance, must be appropriate for the given social situation. This becomes a greater challenge in playful and unstructured interactions, such as those involving children.

Recent feasibility studies involving children with autism spectrum disorders (ASD) interacting with socially assistive robots have shown that some children have positive reactions to robots, while others may have negative reactions. It is unlikely that children with ASD will enjoy any robot 100% of the time. It is therefore important to develop methods for detecting negative child behaviors in order to minimize distress and facilitate effective human-robot interaction. Our past work has shown that negative reactions can be readily identified and classified by a human observer from overhead video data alone, and that an automated position tracker combined with human-determined heuristics can differentiate between the two classes of reactions. This paper describes and validates an improved, non-heuristic method for determining if a child is interacting positively or negatively with a robot, based on Gaussian mixture models (GMM) and a naive-Bayes classifier of overhead camera observations. The approach achieves a 91.4% accuracy rate in classifying robot interaction, parent interaction, avoidance, and hiding against the wall behaviors and demonstrates that these classes are sufficient for distinguishing between positive and negative reactions of the child to the robot.

Several machine learning techniques are used to model the behavior of children with autism interacting with a humanoid robot, comparing a static model to a dynamic model using hand-coded features. Good accuracy (over 80%) is achieved in predicting child vocalizations; directions for future approaches to modeling the behavior of children with autism are suggested.

Socially Assistive Robotics (SAR) defines the research regarding robots which provide assistance to users through social interaction [Feil-Seifer and Matari´ 2005]. Socially assistive robots are being studied for therapeutic use with children with autism spectrum disorders (ASD). It has been observed that children with ASD interact with robots differently than with people or toys. This may indicate an intrinsic interest in such machines, which could be applied as a robot augmentation for an intervention for children with ASD. Preliminary studies suggest that robots may act as intrinsicallyrewarding social partners for children with autism. However, enabling a robot to understand social behavior, and do so while interacting with the child, is a challenging problem. Children are highly individual and thus technology used for social interaction requires recognition of a wide-range of social behavior. This work addresses the challenge of designing behaviors for socially assistive robots in order to enable them to recognize and appropriately respond to a childs free-form behavior in unstructured play contexts. The focus on free-form behavior is inspired by and grounded in existing approaches to therapeutic intervention with children with ASD. This model emphasizes creating circles of communication and fostering engagement through play. A key aspect of this approach is to recognize social behavior and use engagements to bolster social interaction behavior, and to study the ethical implications of therapeutic robotics applications. This research will present a methodology and a validated experimental framework for enabling fully autonomous robots to interact with both typically developing children and children with autism spectrum disorders (ASD) in undirected scenarios using socially appropriate behavior especially where spatial interaction is concerned. This work holds autonomous operation as a critical aspect of the development and implementation of a robot system. Save for safety interventions by a human operator, the robot system presented in this work acts of its own accord. The methodology of this work holds that free-form interaction is best served by allowing a child to move about a space as they choose, and we wish to enable a robot that can allow for such freedom and function effectively for its interaction goals. As such, the robot and child interact, in part, though distance-oriented behavior, and the robot must be able to recognize those behaviors and appropriately respond to them. An overarching goal of this work is to develop a methodology which did not preclude human-human interaction, and in fact encourages human-human interaction. We wish to use this system was to be used as an augmentation, rather than a replacement for a human therapist. There is no substitute for human-human interaction in social interaction. However, the compelling interaction between children with ASD and robots is encouraging for their use as a therapeutic aid. This work aims for the following with an eye toward therapeutic potential: • Detection and mitigation of a childs distress: we define a methodology for learning and applying a datadriven spatio-temporal model of social behavior based on proxemic features to automatically differentiate between typical child-robot interactive behavior and behavior that would suggest an aversive response. Using a Gaussian Mixture Model learned over proxemic feature data the developed system is able to detect and interpret social behavior of the child with sufficient accuracy to recognize distress on the part of the child. The robot uses this model to change its own behavior to encourage positive social interaction [Feil-Seifer and Matari´ c,

Recent feasibility studies involving children with autism spectrum disorders (ASD) interacting with socially assistive robots have shown that children can have both positive and negative reactions to robots. These reactions can be readily identified by a human observer watching videos from an overhead camera. Our goal is to automate the process of such behavior analysis. This paper shows how a heuristic classifier can be used to discriminate between children that are attempting to interact socially with a robot and children that are not.

Our research is the exploration of the social effects of human-robot interaction (HRI) on children with ASD, a population that has deficiencies in many types of social behavior. Computers and robots have been shown to be a catalyst for increased social interaction in children with ASD, yet that effect requires further study to be effectively employed as a therapeutic intervention.

This paper describes a novel control architecture, B3IA, designed to address the challenges of developing autonomous robot systems for use as behavior intervention tools for children with autism spectrum disorders (ASD). Our goal is to create a system that can be easily adapted for use by non-roboticists engaged in ASD therapy. B3IA is a behavior-based architecture for control of socially assistive robots using human-robot interaction in the ASD context. We hypothesize that the organization of a robot control architecture is important to the success of a robot-assisted intervention, because the success of such intervention hinges on the behavior of the robot. We detail the organization of B3IA and present preliminary results from experiments that begin to experimentally test this hypothesis.

Achieving and maintaining user engagement is a key goal of human-robot interaction. This paper presents a method for determining user engagement state from physiological data (including galvanic skin response and skin temperature). In the reported study, physiological data were measured while participants played a wire puzzle game moderated by either a simulated or embodied robot, both with varying personalities. The resulting physiological data were segmented and classified based on position within trial using the K-Nearest Neighbors algorithm. We found it was possible to estimate the user's engagement state for trials of variable length with an accuracy of 84.73%. In future experiments, this ability would allow assistive robot moderators to estimate the user's likelihood of ending an interaction at any given point during the interaction. This knowledge could then be used to adapt the behavior of the robot in an attempt to re-engage the user.

In this work, we further test the hypothesis that physical embodiment has a measurable effect on performance and impression of social interactions. Support for this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and would have significant implications for human-robot interaction. We have refined our task-based metrics to give a measurement, not only of the participant's immediate impressions of a coach for a task, but also of the participant's performance in a given task. We measure task performance and participants' impression of a robot's social abilities in a structured task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; and (2) the effect of physical presence through a co-located robot versus a remote, tele-present robot. With a participant pool (n=21) of roboticists and non- roboticists, we were able to show that participants felt that an embodied robot w as more appealing and perceptive of the world than non-embodied robots. A larger pool of participants (n=32) also demonstrated that the embodied robot was seen as most helpful, watchful, and enjoyable when compared to a remote tele-present robot and a simulated robot.

No abstract available.

Autonomous robots are agents with physical bodies that share our environment. In this work, we test the hypothesis that physical embodiment has a measurable effect on performance and perception of social interactions. Support of this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and have significant implications for human-robot interaction. We measure task performance and perception of a robot's social abilities in a structured but open-ended task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; (2) the effect of physical presence through a co-located robot versus a remote tele-present robot. We present data from a pilot study with 12 subjects showing interesting differences in perception of remote physical robot's and simulated agent's attention to the task, and task enjoyment.