Autism spectrum disorders are a group of lifelong disabilities that affect people's ability to communicate and to understand social cues. Research into applying robots as therapy tools has shown that robots seem to improve engagement and elicit novel social behaviors from people (particularly children and teenagers) with autism. Robot therapy for autism has been explored as one of the first application domains in the field of socially assistive robotics (SAR), which aims to develop robots that assist people with special needs through social interactions. In this review, we discuss the past decade's work in SAR systems designed for autism therapy by analyzing robot design decisions, human-robot interactions, and system evaluations. We conclude by discussing challenges and future trends for this young but rapidly developing research area.

This article reviews the state of the art in social eye gaze for human-robot interaction (HRI). It establishes three categories of gaze research in HRI, defined by differences in goals and methods: a human-centered approach, which focuses on people's responses to gaze; a design-centered approach, which addresses the features of robot gaze behavior and appearance that improve interaction; and a technology-centered approach, which is concentrated on the computational tools for implementing social eye gaze in robots. This paper begins with background information about gaze research in HRI and ends with a set of open questions.

In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.Prepared using sagej.cls [Version: 2015/06/09 v1.01] arXiv:1706.00155v1 [cs.RO] 1 Jun 2017 * While we assume the goal is fixed, we do not assume how the user will achieve that goal (e.g. grasp location) is fixed.

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As assistive robots become popular in factories and homes, there is greater need for natural, multi-channel communication during collaborative manipulation tasks. Non-verbal communication such as eye gaze can provide information without overloading more taxing channels like speech. However, certain collaborative tasks may draw attention away from these subtle communication modalities. For instance, robot-to-human handovers are primarily manual tasks, and human attention is therefore drawn to robot hands rather than to robot faces during handovers. In this paper, we show that a simple manipulation of a robot's handover behavior can significantly increase both awareness of the robot's eye gaze and compliance with that gaze. When eye gaze communication occurs during the robot's release of an object, delaying object release until the gaze is finished draws attention back to the robot's head, which increases conscious perception of the robot's communication. Furthermore, the handover delay increases peoples' compliance with the robot's communication over a non-delayed handover, even when compliance results in counterintuitive behavior.