Robots such as those serving as educational tutors, healthcare supporters, and collaborative partners must develop “rapport,” a construct that encompasses mutual understanding and interpersonal connection with people, to ensure their long-term success. In our earlier work, we constructed, evaluated, and validated an 18-item Connection–Coordination Rapport (CCR) scale to measure human–robot rapport (Studies 1–3). Even though the full-length 18-item CCR scale measures rapport thoroughly, it may not always be practical for researchers to adopt given its relatively long length. Therefore, in this work, we developed a reduced-length version of the CCR scale that still effectively measures rapport using just 8 items. Following recommended practices for short-form development and validation, we leveraged the input of Human–Robot Interaction (HRI) experts (Study 4, \(N=30\) ) to shorten the CCR scale from 18 items to 8 items (4 items per factor). Then, we evaluated this reduced-length CCR scale on a new sample (Study 5, \(N=186\) ) where online participants watched a HRI video and evaluated it using both the full-length and reduced-length CCR scales. We validated the reduced-length CCR scale by showing that it has high internal reliability, high overlap with the full-length CCR scale, a consistent factor structure, high construct validity, and significant time savings.

Scales are commonly employed in Human–Robot Interaction (HRI) research, yet due to its multidisciplinary nature, many in this community lack direct training in psychometrics. This poses challenges for appropriate scale selection, accurate assessments of reliability and validity, and use. We provide a tutorial to empower researchers without scale development expertise to assess scale quality efficiently. We detail a guideline that provides high-level questions and examples to help the reader make confident evaluations of existing scales in HRI. The guideline is then used to evaluate the Godspeed and Robotic Social Attributes Scale (RoSAS). RoSAS is found to be adequately validated, whereas Godspeed warrants further investigation before it should be used in HRI contexts. The article concludes by offering advice on the use of custom scales and provides references for further enhancing expertise in this domain.

Determining whether a robot's actions will be perceived as fair or unfair is complicated in Human-Robot Interaction (HRI), where factors like the robot's perceived agency and intent may influence these judgments. We report findings from two experiments that examine how people evaluate fairness after reviewing a scenario where a robot harms a human. In these experiments, we manipulate different aspects of the context: the fairness of a situation (Fair vs. Unfair); the perceived agency of a robot that commits the harm (High Agency vs. Low Agency); and the perceived intention behind the harmful action (Intentional vs. Unintentional). We examine fairness as a multifaceted construct, using Fairness Theory to capture three key components: reduced welfare, conduct, and moral transgression. We find that this multifaceted perspective can capture nuances in fairness judgments. When robots are perceived to have greater decision-making autonomy, humans tend to assign higher moral responsibility, especially when harmful actions appear intentional. Conversely, when robots are seen as merely following predetermined programming, people focus more on the possibility that the programming could have been designed differently. These findings highlight how agency and intention need to be considered when investigating fairness in HRI.

Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing “rapport,” which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named “Connection” and “Coordination.” We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.

There are currently no psychometrically valid tools to measure the perceived danger of robots. To fill this gap, we provided a definition of perceived danger and developed and validated a 12-item bifactor scale through four studies. An exploratory factor analysis revealed four subdimensions of perceived danger: affective states, physical vulnerability, ominousness, and cognitive readiness. A confirmatory factor analysis confirmed the bifactor model. We then compared the perceived danger scale to the Godspeed perceived safety scale and found that the perceived danger scale is a better predictor of empirical data. We also validated the scale in an in-person setting and found that the perceived danger scale is sensitive to robot speed manipulations, consistent with previous empirical findings. Results across experiments suggest that the perceived danger scale is reliable, valid, and an adequate predictor of both perceived safety and perceived danger in human-robot interaction contexts.

The Robotic Social Attributes Scale (RoSAS) is widely used in human-robot interaction research to measure the social perception of robots, including warmth, competence, and discomfort. As previous researchers have found ambiguous support for the RoSAS's three-factor structure, the current study aims to evaluate the proposed structure by conducting a confirmatory factor analysis (CFA) using openly available datasets. The CFA (n = 1107) showed that the three-factor model had a poor model fit. This suggests that the RoSAS's three dimensions might better be used as separate scales instead of measuring a broad concept of social perception. When separating by stimulus type, only stimuli using words and vignettes had an acceptable model fit, indicating that the RoSAS might be more suitable for word/vignette stimuli. We recommend using the RoSAS's individual subscales as separate constructs rather than measuring social attributes in general. This approach also aligns with what most research has already adopted.

Construct measurement scales are commonly employed in HRI research. We provide a half-day tutorial (4 hours) that aims to empower researchers with the tools to find appropriate scales for their research and assess the quality of those scales confidently and efficiently. There are no prerequisites required for attendees. We aim to recruit researchers interested in using scales but who lack confidence in evaluating their development. The first part of the tutorial will teach attendees how to assess the quality of HRI scales. To accomplish this, we will review basic topics in psychometric theory and a guideline (developed by the organizers) that outlines best practices in scale development and validation. In the second part, we will apply this guideline to two frequently used HRI scales: Godspeed and RoSAS. Attendees are also encouraged to bring scales they are interested in reviewing. The third part aims to help attendees find appropriate scales for their research. To accomplish this, we will debut a new HRI scale database we have developed. This database is the first centralized online repository of HRI scales and contains over 40 of the most used and cited HRI scales covering a wide array of topics of interest such as, trust, embodiment, safety, and attitudes towards robots. We will demonstrate how to access and use the information contained within the database. Our goal for this tutorial is to promote active engagement from attendees throughout the session, ultimately striving to improve the quality and replicability of results in HRI studies.

As autonomous robots become integrated into society, they must socially navigate around humans. We propose that effective social robot navigation relies on three key principles: social norms, perceived safety, and legibility. Our framework, Overlapping Social Navigation Principles, suggests that the strength of each principle is influenced by the presence of other principles. To test our framework, we implemented SRN behaviors on an autonomous robot in a passing scenario and conducted an online study where participants ranked videos of different SRN behavior combinations. Our findings show that incorporating all three principles enhances SRN, with social norms having the greatest impact.

Reading code is a fundamental activity in both software engineering and computer science education. Understanding the cognitive processes involved in reading code is crucial for identifying effective cognitive strategies, which can inform teaching methods and tooling support for developers. However, collecting large human subject eye tracking datasets, especially for programming tasks, is often costly and time-consuming, limiting its scalability and applicability. To address this issue, we present CodeACT-R, the first cognitive simulation framework tailored for code reading, based on the well-established Adaptive Control of Thought—Rational (ACT-R) architecture from cognitive science. CodeACT-R simulates how humans read code and requires only a small, manageable amount of human data to initiate the simulator design, offering a cost-effective and scalable alternative to traditional data collection methods like eye tracking.Specifically, we first collected real human visual attention data from 48 programmers reading code using eye tracking. These data were then used to develop CodeACT-R, enabling the simulation of human-like code reading behaviors. Our evaluation demonstrates that CodeACT-R is capable of simulating visual attention patterns (i.e., scanpaths) that closely resemble real-world human attention patterns, also accounting for up to 87% of observed pattern variations.

The perception of danger in HRI settings has become increasingly important as interactions between robots and humans become more commonplace. Previously, a perceived danger scale was developed and validated. Here, we shortened this scale to create the Perceived Danger-Short Form (PD-SF) scale. Experiment 1 used pre-existing data and standard procedures to shorten the scale from 12 items to 4. Experiment 2 validated the short form in a new experiment where participants observed images of robots holding kitchen items of varying levels of danger in close proximity to a human. PD-SF was able to capture differences across the kitchen items. Results from both experiments indicate that PD-SF is a reliable and psychometrically valid measure of perceived danger in HRI contexts.

In this paper, we study the problem of gesture recognition as a method for divers to communicate with an underwater robot. Gesture is a common method of communication between divers, and yet autonomous underwater vehicles have very limited capacity to understand gesture given lighting and visibility constraints (e.g., from water turbidity and diver depth). Traditional deep learning methods are limited in this domain because of a lack of sufficient training data. We show that it is not enough to learn a gesture in a laboratory setting, because the appearance changes dramatically underwater. We show how hyperdimensional computing can solve this problem by permitting hypervectors to serve as abstract representations of gestures, yielding rapid adaptation to new environments and new gestures. We experimentally verify this approach using a novel dataset of 6 diving relevant gestures. We show that we can accurately adapt to a gesture learned in a laboratory setting to work with a gesture observed underwater. Our approach compares favorably to a ResNet-18, which performs well in laboratory conditions (91.9% accuracy), but performs poorly underwater (53.9% accuracy). Our proposed approach is capable of rapid adaptation, resulting in an accuracy of 83.8% on underwater gestures with just one additional example from each class added to the support set. Finally, we also show the ability to adapt to new gestures not present in our original training set. We use hypervectors to learn new gestures from the Sign Language MNIST dataset, providing a high level of accuracy with a limited amount of training data.

This paper demonstrates the effectiveness of learning based models for accurate, and reliable robot to human handoffs in various HRI scenarios. Specifically we bench marked a neuromorphic spiking neural network and a time series k-nearest neighbors classifier against traditional hand crafted force threshold methods. These models use linear force in the x, y, and z direction, as well as torque about the x, y, and z axis at the end effector of the robot arm to make handoff predictions. This paper demonstrates that these learning based methods are more robust to noise which occurs during operational use. We applied our algorithms to both stationary handoffs (stationary robot) and moving handoffs (robot walking). We believe that our evaluation is the first to examine walking handoffs. We evaluated all models in tests which determined the accuracy, precision, recall, f1, and average execution time for handoff events, noise events, and no event tests. We find that the SLAYER spiking neural network model performed the best across both walking and stationary handoffs for the majority of the evaluation criteria. Our results suggest that neuromorphic spiking neural networks are strong contenders for applications in time series, event based HRI applications.

The perception of agency in human robot interaction has become increasingly important as robots become more capable and more social. There are, however, no accepted or consistent methods of measuring perceived agency; researchers currently use a wide range of techniques and surveys. We provide a definition of perceived agency, and from that definition we create and psychometrically validate a scale to measure perceived agency. We then perform a scale evaluation by comparing the PA scale constructed in experiment 1 to two other existing scales. We find that our PA and PA-R (Perceived Agency–Rasch) scales provide a better fit to empirical data than existing measures. We also perform scale validation by showing that our scale shows the hypothesized relationship between perceived agency and morality.

The perception of agency in robots and AI characters has become increasingly important as different agents increase their capabilities. Experiment 1 took an existing measure of perceived agency and created a reduced version by using existing Rasch item reduction measures; Eight and five item scales were created. Experiment 2 showed that all three scales (PA, PA8, PA5) were able to capture differences in perceived agency between a cheating robot (higher PA) and a non-cheating robot (lower PA). Experiment 3 showed that all three scales were able to show the predicted positive relationship between perceived agency and perceived moral agency. All three scales also showed high internal validity. Suggestions for the usage of the scales was also discussed.

Learning to recognize new objects in real time in unconstrained environments presents significant challenges for robotic platforms. We present a meta-learning solution to this problem as well as a registered image and events dataset to facilitate work in this domain. Our solution uses interactive motion to isolate the object, and motion-based saliency (from events) to select relevant keypoints from a high-resolution RGB image. Salient keypoints are then passed to a meta-learner to classify the object type. We show that using our interactive isolation and keypoint selection approach, we outperform existing techniques by 6-20%.

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Prior research in human-robot teamwork proposes fairness definitions for human-robot teaming that are based on the work completed by each team member. However, metrics that properly capture people’s perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people’s perception of fairness. First, we extend prior fairness metrics based on team members’ capabilities and workload to a bigger team. We also develop a new metric to quantify the amount of time that the robot spends working on the same task as each person. We conduct an online user study (n=95) and show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people’s assessment of fairness. When asked to rate fairness based on the amount of time that the robot spends working with each person, participants used two factors (fairness based on the robot’s time and teammates’ capabilities). This bleed-over effect is stronger when people are asked to assess fairness based on capability. From these insights, we propose design guidelines for algorithms to enable robotic teammates to consider fairness in its decision-making to maintain positive team social dynamics and team task performance.

No abstract available.

Social perceivers often view a human agent's norm-violating behavior as diagnostic of that person's mental states, while behaviors that conform to norms are viewed as less informative. We developed a series of stimulus videos depicting a DRC-HUBO robot engaging in norm-violating and norm-conforming behaviors. We explored the hypothesis that robots' norm-violating actions may invite social perceivers to increase their mental state attributions in a similar manner as they do in humans. Surprisingly, we found that norm-conforming behaviors appear to be at least as conducive as norm-violating behaviors, and perhaps even moreso, to mental state attribution to robotic agents.

We developed and evaluated a novel humanoid head, SCIPRR (Sensing, Computing, Interacting Platform for Robotics Research). SCIPRR is a head shell that was iteratively created with additive manufactur- ing. SCIPRR contains internal sca olding that allows sensors, small form computers, and a back-projection system to display an ani- mated face on a front-facing screen. SCIPRR was developed using User Centered Design principles and evaluated using three di erent methods. First, we created multiple, small-scale prototypes through additive manufacturing and performed polling and re nement of the overall head shape. Second, we performed usability evaluations of expert HRI mechanics as they swapped sensors and computers within the the SCIPRR head. Finally, we ran and analyzed an ex- periment to evaluate how much novices would like a robot with our head design to perform di erent social and traditional robot tasks. We made both major and minor changes a er each evalu- ation and iteration. Overall, expert users liked the SCIPRR head and novices wanted a robot with the SCIPRR head to perform more tasks (including social tasks) than a more traditional robot.

The ability to perform robust, precise, real-time visual recognition is extremely critical for the use of robotic systems in real-world applications. This paper explores the use of Convolution Neural Networks (CNN) and human assisted training in teaching a robot to recognize novel objects. We investigated the impact of providing instructions to a human teacher during a training scenario for novel objects. Participants in the naïve condition were provided verbal instructions by the robot, and participants in the embodied condition were provided embodied demonstrations by the robot. The results showed that a vision system trained by participants with embodied instructions clearly outperformed a system trained by naïve participants. The latest computer vision techniques combined with human assisted teaching was found to provide excellent results for novel object recognition.

Social HRI requires robots able to use appropriate, adaptive and contingent behaviours to form and maintain engaging social interactions with people. Cognitive Architectures emphasise a generality of mechanism and application, making them an ideal basis for such technical developments. Following the successful first workshop on Cognitive Architectures for HRI at the 2014 HRI conference, this second edition of the workshop focusses specifically on applications to social interaction. The full-day workshop is centred on participant contributions, and structured around a set of questions to provide a common basis of comparison between different assumptions, approaches, mechanisms, and architectures. These contributions will be used to support extensive and structured discussions, with the aim of facilitating the development and application of cognitive architectures to social HRI systems. By attending, we envisage that participants will gain insight into how the consideration of cognitive architectures complements the development of autonomous social robots.

Many interfaces have been designed to prevent or reduce errors. These interfaces may, in fact, reduce the error rate of specific error classes, but may also have unintended consequences. In this paper, we show a series of studies where a better interface did not reduce the number of errors but instead shifted errors from one error class (omissions) to another error class (perseverations). We also show that having access to progress tracking (a progress bar) does not reduce the number of errors. We propose and demonstrate a solution -- a predictive error system -- that reduces errors based on the error class, not on the type of interface.

Developments in autonomous agents for Human-Robot Interaction (HRI), particularly social, are gathering pace. The typical approach to such efforts is to start with an application to a specific interaction context (problem, task, or aspect of interaction) and then try to generalise to different contexts. Alternatively however, the application of Cognitive Architectures emphasises generality across contexts in the first instance. While not the "silver-bullet" solution, this perspective has a number of advantages both in terms of the functionality of the resulting systems, and indeed in the process of applying these ideas. Centred on invited talks to present a range of perspectives, this workshop provides a forum to introduce and discuss the application (both existing and potential) of Cognitive Architectures to HRI, particularly in the social domain. Participants will gain insight into how such a consideration of Cognitive Architectures complements the development of autonomous social robots.

In this paper, we describe a large-scale (over 4000 participants) observational field study at a public venue, designed to explore how social a robot needs to be for people to engage with it. In this study we examined a prediction of Computers Are Social Actors (CASA) framework: the more machines present human-like characteristics in a consistent manner, the more likely they are to invoke a social response. Our humanoid robot's behavior varied in the amount of social cues, from no active social cues to increasing levels of social cues during story-telling to human-like game-playing interaction. We found several strong aspects of support for CASA: the robot that provides even minimal social cues (speech) is more engaging than a robot that does nothing, and the more human-like the robot behaved during story-telling, the more social engagement was observed. However, contrary to the prediction, the robot's game-playing did not elicit more engagement than other, less social behaviors.

Crandalland Cummings & Mitchell introduced fan-out as a measure of the maximum number of robots a single human operator can supervise in a given single-human-multiple-robot system. Fan-out is based on the time constraints imposed by limitations of the robots and of the supervisor, e.g., limitations in attention. Adapting their work, we introduced a dynamic model of operator overload that predicts failures in supervisory control in real time, based on fluctuations in time constraints and in the supervisor's allocation of attention, as assessed by eye fixations. Operator overload was assessed by damage incurred by unmanned aerial vehicles when they traversed hazard areas. The model generalized well to variants of the baseline task. We then incorporated the model into the system where it predicted in real time, when an operator would fail to prevent vehicle damage and alerted the operator to the threat at those times. These model-based adaptive cues reduced the damage rate by one-half relative to a control condition with no cues.

Adaptive automation (AA) can improve performance while addressing the problems associated with a fully automated system. The best way to invoke AA is unclear, but two ways include critical events and the operator's state. A hybrid model of AA invocation, the dynamic model of operator overload (DMOO), that takes into account critical events and the operator's state was recently shown to improve performance. The DMOO initiates AA using critical events and attention allocation, informed by eye movements. We compared the DMOO with an inaccurate automation invocation system and a system that invoked AA based only on critical events. Fewer errors were made with DMOO than with the inaccurate system. In the critical event condition, where automation was invoked at an earlier point in time, there were more memory and planning errors, while for the DMOO condition, which invocated automation at a later point in time, there were more perceptual errors. These findings provide a framework for reducing specific types of errors through different automation invocation.

We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous empirically validated examples of ACT-R/E models.

This video submission demonstrates cooperative human-robot firefighting. A human team leader guides the robot to the fire using a combination of speech and gesture.

We built and evaluated a predictive model for resuming after an interruption. Two different experiments were run. The first experiment showed that people used a transactive memory process, relying on another person to keep track of where they were after being interrupted while retelling a story. A memory for goals model was built using the ACT-R/E cognitive architecture that matched the cognitive and behavioral aspects of the experiment. In a second experiment, the memory for goals model was put on an embodied robot that listened to a story being told. When the human storyteller attempted to resume the story after an interruption, the robot used the memory for goals model to determine if the person had forgotten the last thing that was said. If the model predicted that the person was having trouble remembering the last thing said, the robot offered a suggestion on where to resume. Signal detection analyses showed that the model accurately predicted when the person needed help.

The variability of human behavior during plan execution poses a difficult challenge for human-robot teams. In this paper, we use the concepts of theory of mind to enable robots to account for two sources of human variability during team operation. When faced with an unexpected action by a human teammate, a robot uses a simulation analysis of different hypothetical cognitive models of the human to identify the most likely cause for the human's behavior. This allows the cognitive robot to account for variances due to both different knowledge and beliefs about the world, as well as different possible paths the human could take with a given set of knowledge and beliefs. An experiment showed that cognitive robots equipped with this functionality are viewed as both more natural and intelligent teammates, compared to both robots who either say nothing when presented with human variability, and robots who simply point out any discrepancies between the human's expected, and actual, behavior. Overall, this analysis leads to an effective, general approach for determining what thought process is leading to a human's actions.

Procedural errors occur despite the user having the correct knowledge of how to perform a particular task. Previous research has mostly focused on preventing these errors by redesigning tasks to eliminate error prone steps. A differentmethod of preventing errors, specifically postcompletion errors (e.g., forgetting to retrieve the original document from a photocopier), has been proposed by Ratwani, McCurry, and Trafton (2008), which uses theoretically motivated eye movement measures to predict when a user will make an error. The predictive value of the eye-movement-based model was examined and validated on two different tasks using a receiver-operating characteristic analysis. A real-time eye-tracking postcompletion error pre-diction system was then developed and tested; results demonstrate that the real-time system successfully predicts and prevents postcompletion errors before a user commits the error. 1.

Robots are becoming part of people's everyday social lives - and will increasingly become so. In future years, robots may become caretaking assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. They may become our friends. As we move to create our future with robots, hard problems in HRI exist, both technically and socially. The Fifth Annual Conference on HRI seeks to take up grand technical and social challenges in the field - and speak to their integration. This panel brings together 4 leading experts in the field of HRI to speak on this topic.

It is expected that in the near-future people will have daily natural language interactions with robots. However, we know very little about how users feel they should talk to robots, especially users who have never before interacted with a robot. The present study evaluated first-time users' expectations about a robot's cognitive and communicative capabilities by comparing robot-directed speech to the way in which participants talked to a human partner. The results indicate that participants spoke more loudly, raised their pitch, and hyperarticulated their messages when they spoke to the robot, suggesting that they viewed the robot as having low linguistic competence. However, utterances show that speakers often assumed that the robot had humanlike cognitive capabilities. The results suggest that while first-time users were concerned with the fragility of the robot's speech recognition system, they believed that the robot had extremely strong information processing capabilities.

For a single operator to effectively control multiple robots, operator situation awareness is a critical component of the human-robot system. There are three levels of situation awareness: perception, comprehension, and projection into the future [1]. We focus on the perception level to develop a theoretic model of the perceptual-cognitive processes underlying situation awareness. Eye movement measures were developed as indicators of cognitive processing and these measures were used to account for operator situation awareness on a supervisory control task. The eye movement based model emphasizes the importance of visual scanning and attention allocation as the cognitive processes that lead to operator situation awareness and the model lays the groundwork for real-time prediction of operator situation awareness.

A 3D optical flow tracking system was used to track participants as they watched a series of boring videos. The video stream of the participants was rated for boredom events. Ratings and head position data were combined to predict boredom events.

Decades of research have shown that speakers adapt the way in which they speak to meet the needs of listeners, and that speech modifications can illuminate speakers' conceptualizations of their listeners' cognitive and communicative abilities. The present study extends this line of research into human-robot communication by analyzing the linguistic features of commands given to a robotic dog. The results indicate that males and females differed in the way in which they spoke to the robot, suggesting that there was not a uniform expectation of the robot's communicative capacities.

Tracking people and objects is an enabling technology for many robotic applications. From human-robot-interaction to SLAM, robots must know what a scene contains and how it has changed, and is changing, before they can interact with their environment. In this paper, we focus on the tracking necessary to record the 3D position and pose of objects as they change in real time. We develop a tracking system that is capable of recovering object locations and angles at speeds in excess of 60 frames per second, making it possible to track people and objects undergoing rapid motion and acceleration. Results are demonstrated experimentally using real objects and people and compared against ground truth data.

A postcompletion error is a distinct type of procedural error where one fails to complete the final step of a task. While redesigning interfaces and providing explicit cues have been shown to be effective in reducing the postcompletion error rate, these methods are not always feasible or well liked. This paper demonstrates how specific eye movement measures can be used to predict when a user will make a postcompletion error. We describe a real-time eye gaze system that provides cues to the user if and only if there is a high probability of the user making a postcompletion error.

We describe a computational cognitive architecture for robots which we call ACT-R/E (ACT-R/Embodied). ACT-R/E is based on ACT-R [1, 2] but uses different visual, auditory, and movement modules. We describe a model that uses ACT-R/E to integrate visual and auditory information to perform conversation tracking in a dynamic environment. We also performed an empirical evaluation study which shows that people see our conversational tracking system as extremely natural.

The number and type of unmanned vehicles sought in military operations continues to grow. A critical consideration in designing these systems is identifying interface types or interaction schemes that enhance an operator's ability to supervise multiple unmanned vehicles. Past research has explored how interface types impact overall performance measures (e.g. mission execution time), but has not extensively examined other human performance factors that might influence human-robot interaction. Within a dynamic military environment, it is particularly important to assess how interfaces impact an operator's ability to quickly adapt and alter the unmanned vehicle's tasking. To assess an operator's ability to confront this changing environment, we explored the impact of interface type on task switching. Research has shown performance costs (i.e. increased time response) when individuals switch between different tasks. Results from this study suggest that this task switching effect is also seen when participants controlling multiple unmanned vehicles switch between different strategies. Results also indicate that when utilizing a flexible delegation interface, participants did not incur as large a switch cost effect as they did when using an interface that allowed only the use of fixed automated control of the unmanned vehicles.

How do children learn how to play hide and seek? At age 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. We show through a case study that a 3 1/2 year old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. We propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. We model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. We also take the "hiding" model and use it as the basis for a "seeking" model. We suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

The Peer-To-Peer Human-Robot Interaction (P2P-HRI) project is developing techniques to improve task coordination and collaboration between human and robot partners. Our work is motivated by the need to develop effective human-robot teams for space mission operations. A central element of our approach is creating dialogue and interaction tools that enable humans and robots to flexibly support one another. In order to understand how this approach can influence task performance, we recently conducted a series of tests simulating a lunar construction task with a human-robot team. In this paper, we describe the tests performed, discuss our initial results, and analyze the effect of intervention on task performance.

We propose that an important aspect of human-robot interaction is perspective-taking. We show how perspective-taking occurs in a naturalistic environment (astronauts working on a collaborative project) and present a cognitive architecture for performing perspective-taking called Polyscheme. Finally, we show a fully integrated system that instantiates our theoretical framework within a working robot system. Our system successfully solves a series of perspective-taking problems and uses the same frames of references that astronauts do to facilitate collaborative problem solving with a person.

In our research on intuitive means for humans and intelligent, mobile robots to collaborate, we use a multimodal interface that supports speech and gestural inputs. As a preliminary step to evaluate our approach and to identify practical areas for future work, we conducted a wizard-of-Oz pilot study with five participants who each collaborated with a robot on a search task in a separate room. The goal was to find a sign in the robot's environment with the word "FOO" printed on it. Using a subset of our multimodal interface, participants were told to direct the collaboration. As their subordinate, the robot would understand their utterances and gestures, and recognize objects and structures in the search space. Participants conversed with the robot through a wireless microphone and headphone and, for gestural input, used a touch screen displaying alternative views of the robot's environment to indicate locations and objects.

The purpose of this paper is to introduce a hybrid cognitive-reactive system, which integrates a machine-learning algorithm (SAMUEL, an evolutionary algorithm-based rule-learning system) with a computational cognitive model (written in ACT-R). In this system, the learning algorithm handles reactive aspects of the task and provides an adaptation mechanism, while the cognitive model handles cognitive aspects of the task and ensures the realism of the behavior. In this study, the controller architecture is used to implement a controller for a team of micro-air vehicles performing reconnaissance and surveillance.

We explore the effects of interfaces to take notes on problem solving and learning in a scientific discovery domain. In 2 experiments (1 correlational, 1 experimental), participants solved a series of 5 scientific reasoning problems in a computer environment. We provided some participants with access to an online notepad and found 3 main results: (a) Using the notepad helped participants solve the problems more accurately; (b) the benefits of using the notepad persisted after participants had stopped using it; and (c) participants who used the notepad for problem solving and self-explanation learned more, regardless of the type of notepad interface that was provided. Implications for learning systems with online notepads are discussed.