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.

Autonomous vehicle technology is rapidly developing but the current state-of-the-art still has limitations and requires frequent human intervention. However, handovers from an autonomous vehicle to a human driver are challenging because a human operator may be unaware of the vehicle surroundings during a handover which can lead to dangerous driving outcomes. There is presently an urgent need to develop advanced driver-assistance systems capable of monitoring driver situational awareness within an autonomous vehicle and intelligently handing-over control to a human driver in emergency situations. Towards this goal, in this paper we present the development and evaluation of a vision-based system that identifies visual cues of a driver's situational awareness including their: head pose, eye pupil position, average head movement rate and visual focus of attention.