Learning from demonstration (LfD) approaches have garnered significant interest for teaching social robots a variety of tasks in healthcare, educational, and service domains after they have been deployed. These LfD approaches often require a significant number of demonstrations for a robot to learn a performant model from task demonstrations. However, requiring non-experts to provide numerous demonstrations for a social robot to learn a task is impractical in real-world applications. In this paper, we propose a method to improve the sample efficiency of existing learning from demonstration approaches via data augmentation, dynamic experience replay sizes, and hierarchical Deep Q-Networks (DQN). After validating our methods on two different datasets, results suggest that our proposed hierarchical DQN is effective for improving sample efficiency when learning tasks from demonstration. In the future, such a sample-efficient approach has the potential to improve our ability to apply LfD approaches for social robots to learn tasks in domains where demonstration data is limited, sparse, and imbalanced.

Parents of children with developmental disabilities face significantly higher workloads than parents of neurotypical children due to their higher care giving demands. Consequently, parents of children with developmental disabilities often face emotional, physical, mental, and social health declines. Currently there has been significant research and development of robots for providing care to children with developmental disabilities to address a variety of care giving scenarios. However, it is presently unclear whether parents would be comfortable with robots interacting with their children in these different child-robot interaction scenarios. In this paper, we investigate parental comfort toward robots caring for children with developmental disabilities in a variety of interaction scenarios and the influence of parental negative attitudes toward robots as well as trust on their comfort toward robots in these scenarios. Overall, our findings suggest that US parental attitudes, trust, and comfort toward robots caring for children with developmental disabilities are neutral. Parents were most comfortable with a robot serving as a teaching assistant to children with a developmental disability and least comfortable as a bus driver. Furthermore, trust for robots had a medium positive association with comfort with child-robot interactions and negative attitudes toward robots had a medium negative association with comfort with child-robot interactions.

Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children’s learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggests that children with ASD’s learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions.

Socially Assistive Robots (SARs) have demonstrated success in the delivery of interventions to individuals with Autism Spectrum Disorder (ASD). To date, these robot-mediated interventions have primarily been designed and implemented by robotics researchers. It remains unclear whether therapists could independently utilize robots to deliver therapies in clinical settings. In this paper, we conducted a study to investigate whether therapists could design and implement robot-mediated interventions for children with ASD. Furthermore, we compared therapists’ performance, efficiency, and perceptions towards using a Virtual Reality (VR) and kinesthetic-based interface for delivering robot-mediated interventions. Overall, our results demonstrated therapists could independently design and implement interventions with a SAR. They were faster at designing a new intervention using VR than a kinesthetic interface. Therapists also had similar performance to delivering inperson interventions when utilizing VR to deliver interventions with the robot. Therapists reported moderate workload using the VR interface and perceived VR to be usable.

Current studies have demonstrated that Socially Assistive Robots (SARs) delivering Applied Behavior Analysis (ABA) based interventions can teach individuals with Autism Spectrum Disorder (ASD) valuable social, emotional, communication and academic skills. These robot-mediated interventions (RMIs) are typically delivered via teleoperation, which places additional or similar workloads on therapists as administering interventions directly. The autonomous delivery of ABA therapies to individuals with ASD by a robot could significantly reduce workload and improve the usability as well as acceptance of this technology. However, pre-programming the autonomy of a SAR with a limited set of interventions is not sufficient for clinical practice due to the rapidly changing and different learning needs of individuals with ASD. In order to be applicable in clinical settings, therapists must be capable of customizing and personalizing interventions to the needs of each individual. Towards this goal, in this paper we present the initial development and deployment of a proof-of-concept Learning from Demonstration (LfD) system in-the-wild to learn the verbal behavior of therapists during the delivery of an ABA-based intervention to children with ASD. We also present preliminary data on the results of a policy trained on data collected from demonstrations provided during this in-the-wild deployment of our LfD system.

Socially assistive robots (SARs) are currently being developed to assist in the delivery of Applied Behavior Analysis (ABA) therapies to individuals diagnosed with Autism Spectrum Disorder (ASD). Although SARs have demonstrated positive outcomes, minimal research has focused on investigating needs of the therapists that deliver treatments. Therapist perspectives are important as they will likely be the primary end-users of SARs. In this study, we investigated the perceptions and design requirements of ABA therapists towards SARs and the interfaces used to operate them. Therapists were interviewed after they independently designed, developed, and implemented their own robot-mediated interventions. Overall, therapists’ general perceptions towards integrating a SAR within their existing workflow was positive and they expected that children would benefit from ABA therapies delivered by a SAR. The therapists also provided insights on design requirements for utilizing SARs and their interfaces as well as potential clinical and future use cases for this technology.