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This paper discusses the emergence of sensorimotor coordination for ESCHeR, a 4DOF redundant foveated robot-head, by interaction with its environment. A feedback-error-learning(FEL)-based distributed control provides the system with explorative abilities with reflexes constraining the learning space. A Kohonen network, trained at run-time, categorizes the sensorimotor patterns obtained over ESCHeR's interaction with its environment, enables the reinforcement of frequently executed actions, thus stabilizing the learning activity over time. We explain how the development of ESCHeR's visual abilities (namely gaze fixation and saccadic motion), from a context-free reflex-based control process to a context-dependent, pattern-based sensorimotor coordination can be related to the Piagetian 'stage theory'.

Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health--related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally. [ABSTRACT FROM AUTHOR]

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use. [ABSTRACT FROM AUTHOR]

Empathy is the ability to share someone else's feelings or experiences; it influences how people interact and relate. Socially assistive robots (SAR) are a promising means of conveying and eliciting empathy toward facilitating human-robot interaction. This work examines factors that influence the amount of empathy elicited by a SAR storyteller and users' perceptions of that robot. We conducted an empirical mixed-design study (N=46) using an autonomous SAR storyteller that told three stories, each with a different human or robot target of empathy. The robot storyteller used the first-person narrative voice (1PNV) with half of the participants and the third-person narrative voice (3PNV) with the other half. We found that the SAR storyteller elicited significantly more empathy when the story target of empathy matched the SAR narrator, i.e., was also a robot. Additionally, the 1PNV robot elicited significantly more empathy and was perceived as more human-like, easy to interact with, and trustworthy than the 3PNV robot. Finally, participants who empathized more with the robot displayed facial expressions consistent with the emotional story content. These insights inform the design of SAR storytellers capable of eliciting empathy toward creating compelling and effective human-robot interactions. [ABSTRACT FROM AUTHOR]

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders (ASD). However, current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions. To bridge this gap, this work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions. By training machine learning (ML) algorithms with supervised domain adaptation (s-DA), the personalized models were able to tradeoff between the limited individual data and the more abundant less personal data pooled from other study participants. We evaluated the effects of personalization on a long-term multimodal dataset consisting of four children with ASD with a total of 19 sessions, and derived inter-rater reliability (IR) scores for binary arousal (IR = 83%) and valence (IR = 81%) labels between human annotators. Our results show that personalized Gradient Boosted Decision Trees (XGBoost) models with s-DA outperformed two non-personalized individualized and generic model baselines not only on the weighted average of all sessions, but also statistically (p <.05) across individual sessions. This work paves the way for the development of personalized autonomous SAR systems tailored toward individuals with atypical cognitive-affective and socio-emotional needs. [ABSTRACT FROM AUTHOR]

Abstract Background Although there is a great deal of success in rehabilitative robotics applied to patient recovery post stroke, most of the research to date has dealt with providing physical assistance. However, new rehabilitation studies support the theory that not all therapy need be hands-on. We describe a new area, called socially assistive robotics, that focuses on non-contact patient/user assistance. We demonstrate the approach with an implemented and tested post-stroke recovery robot and discuss its potential for effectiveness. Results We describe a pilot study involving an autonomous assistive mobile robot that aids stroke patient rehabilitation by providing monitoring, encouragement, and reminders. The robot navigates autonomously, monitors the patient's arm activity, and helps the patient remember to follow a rehabilitation program. We also show preliminary results from a follow-up study that focused on the role of robot physical embodiment in a rehabilitation context. Conclusion We outline and discuss future experimental designs and factors toward the development of effective socially assistive post-stroke rehabilitation robots.

Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user's data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community. [ABSTRACT FROM AUTHOR]

The authors examine the roles of robots for childcare applications. They cite the argument of N. Sharkey and A. Sharkey on the ethical aspect of robot nannies and presents counter argument. They mention that there are many reasons why childcare robotics could lead to neglect on the part of the parents but the matter is not only for robots. They also suggest that parents, roboticists, and legislators should remain vigilant about ethical issues.

Socially assistive robotics (SAR) is a growing area of research. Evaluating SAR systems presents novel challenges. Using a robot for a socially assistive task can have various benefits and ethical implications. Many questions are important to understanding whether a robot is effective for a given application domain. This paper describes several benchmarks for evaluating SAR systems. There exist numerous methods for evaluating the many factors involved in a robot’s design. Benchmarks from psychology, anthropology, medicine, and human–robot interaction are proposed as measures of success in evaluating a given SAR system and its impact on the user and broader population. [ABSTRACT FROM AUTHOR]

The article focuses on the interdisciplinary research that study the human-robot interaction for socially assistive robotic applications. The robotics community is trying to create robots capable of exhibiting social qualities. The field has aimed on the use of the robot's physical embodiment to communicate and interact with users in a social manner. Furthermore, the principle application domains of socially assistive robotics is discussed.

Background: Although there is a great deal of success in rehabilitative robotics applied to patient recovery post stroke, most of the research to date has dealt with providing physical assistance. However, new rehabilitation studies support the theory that not all therapy need be hands-on. We describe a new area, called socially assistive robotics, that focuses on non-contact patient/user assistance. We demonstrate the approach with an implemented and tested post-stroke recovery robot and discuss its potential for effectiveness. Results: We describe a pilot study involving an autonomous assistive mobile robot that aids stroke patient rehabilitation by providing monitoring, encouragement, and reminders. The robot navigates autonomously, monitors the patient's arm activity, and helps the patient remember to follow a rehabilitation program. We also show preliminary results from a follow-up study that focused on the role of robot physical embodiment in a rehabilitation context. Conclusion: We outline and discuss future experimental designs and factors toward the development of effective socially assistive post-stroke rehabilitation robots. [ABSTRACT FROM AUTHOR]

This paper considers the problem of human evacuation assistance. We discuss how this kind of task differs from the more prevalent search and rescue tasks, and the resulting implications for the design of assistive evacuation systems. We describe the implementation and evaluation of an algorithm for deploying audio navigational cues throughout an office building with a team of mobile robots. A review of evacuation dynamics methods is presented and particular methods are applied on-line during deployment. We use a pedestrian simulation and a simple model of audio–evacuee interaction to show the effects of beacon deployment; the results indicate that even a small number of beacons can significantly decrease the mean and variance of egress time and distance. We also advance uses for human motion-based measures of environment complexity for general mobile robotics. [ABSTRACT FROM AUTHOR]

We study the problem of achieving global behavior in a group of distributed robots using only local sensing and minimal communication, in the context of formations. The goal is to have N mobile robots establish and maintain some predetermined geometric shape. We report results from extensive simulation experiments, and 40+ experiments with four physical robots, showing the viability of our approach. The key idea is that each robot keeps a single friend at a desired angle θ, using some appropriate sensor. By panning the sensor by θ degrees, the goal for all formations becomes simply to center the friend in the sensor's field of view. We also present a general analytical measure for evaluating formations and apply it to the position data from both simulation and physical robot experiments. We used two lasers to track the physical robots to obtain ground truth validation data. [ABSTRACT FROM AUTHOR]

The key to utilizing the potential of multirobot systems is cooperation. How can we achieve cooperation in systems composed of failure-prone autonomous robots operating in noisy, dynamic environments? In this paper, we present a novel method of dynamic task allocation for groups of such robots. We implemented and tested an auction-based task allocation system which we call MURDOCH, built upon a principled, resource centric, publish/subscribe communication model. A variant of the Contract Net Protocol, MURDOCH produces a distributed approximation to a global optimum of resource usage. We validated MURDOCH in two very different domains: a tightly coupled multirobot physical manipulation task and a loosely coupled multirobot experiment in long-term autonomy. The primary contribution of this paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems. [ABSTRACT FROM AUTHOR]

We describe localization-space trails (LOST), a method that enables a team of robots to navigate between places of interest in an initially unknown environment using a trail of landmarks. The landmarks are not physical; they are waypoint coordinates generated online by each robot and shared with teammates. Waypoints are specified in each robot's local coordinate system, and contain references to features in the world that are relevant to the team's task and common to all robots. Using these task-level references, robots can share waypoints without maintaining a global coordinate system. The method is tested in a series of real-world multirobot experiments. The results demonstrate that the method: 1) copes with accumulating odometry error; 2) is robust to the failure of individual robots; 3) converges to the best route discovered by any robot in the team. In one experiment, a team of four autonomous mobile robots performs a resource transportation task in our uninstrumented ofrice building. Despite significant divergence of their local coordinate systems, the robots are able to share waypoints, forming and following a common trail between two predetermined locations for more than three hours, traveling a total of 8.2 km (5.1 miles) before running out of power. Designed to scale to large populations, LOST is fully distributed, with low costs in processing, memory, and bandwidth. It combines metric data about the position of features in the world with instructions on how to get from one place to another; producing something between a map and a plan. [ABSTRACT FROM AUTHOR]

Presents a behavior-based approach to structuring and controlling complex robotic systems in the U.S. Use of imitation process in creating interaction and learning behaviors; Development of methods for segmenting and classifying visual input to recognize human behavior; Validation of approach to humanoid motor control and imitation.

This article proposes the concept of basis behaviors as ubiquitous general building blocks for synthesizing artificial group behavior in multiagent systems and for analyzing group behavior in nature. We demonstrate the concept through examples implemented both in simulation and on a group of physical mobile robots. The basis behavior set we propose, consisting of avoidance, safe-wandering, following, aggregation, dispersion, and homing, is constructed from behaviors commonly observed in a variety of species in nature. The proposed behaviors are manifested spatially but have an effect on more abstract modes of interaction, including the exchange of information and cooperation. We demonstrate how basis behaviors can be combined into higher-level group behaviors commonly observed across species. The combination mechanisms we propose are useful for synthesizing a variety of new group behaviors, as well as for analyzing naturally occurring ones. [ABSTRACT FROM PUBLISHER]

The article focuses on the socially assistive robotics (SAR) which improved human-robot interaction (HRI) and social interaction in general. SAR serves as a solution to major social problems, which include growing population of older people, rising costs of medical care and education, and shortages of social programs for education and training of people with disabilities. HRI brings together engineering and social science, cognitive science, neuroscience, ethics, and other disciplines directly related to robotics.

Discusses the coordination and learning in multirobot systems. Challenges of the robotics domain; Assessment of the interaction dynamics between robots and their environment; Exploration of the biases and constraints introduced by the system designer. INSET: New directions for New Directions by James A. Hendler..

Intelligent, interactive systems provide assistance by facilitating social interactions rather than by automating physical tasks. [ABSTRACT FROM AUTHOR]

Socially assistive robotics (SAR) aims to address critical areas and gaps in care by automating supervision, coaching, motivation, and companionship aspects of one-on-one interactions with individuals from various large and growing populations, including stroke survivors, the elderly and individuals with dementia, and children with autism spectrum disorders (ASDs). This article examines the ethical challenges of SAR from three points of view (user, caregiver, and peer) using core principles from medical ethics (autonomy, beneficence, nonmaleficence, and justice) to determine how intended and unintended effects of SAR can impact the delivery of care. [ABSTRACT FROM PUBLISHER]