Abstract — This paper defines the research area of socially assistive robotics, focused on assisting people through social interaction. While much attention has been paid to robots that provide assistance to people through physical contact (which we call contact assistive robotics), and to robots that entertain through social interaction (social interactive robotics), so far there is no clear definition of socially assistive robotics. We summarize active social assistive research projects and classify them by target populations, application domains, and interaction methods. While distinguishing these from socially interactive robotics endeavors, we discuss challenges and opportunities that are specific to the growing field of socially assistive robotics.

In this paper we review the major approaches to Multimodal Human Computer Interaction, giving an overview of the field from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.

Abstract — Robots that work alongside us in our homes and workplaces could extend the time an elderly person can live at home, provide physical assistance to a worker on an assembly line, or help with household chores. In order to assist us in these ways, robots will need to successfully perform manipulation tasks within human environments. Human environments present special challenges for robot manipulation since they are complex, dynamic, uncontrolled, and difficult to perceive reliably. In this paper we present a behavior-based control system that enables a humanoid robot, Domo, to help a person place objects on a shelf. Domo is able to physically locate the shelf, socially cue a person to hand it an object, grasp the object that has been handed to it, transfer the object to the hand that is closest to the shelf, and place the object on the shelf. We use this behavior-based control system to illustrate three themes that characterize our approach to manipulation in human environments. The first theme, cooperative manipulation, refers to the advantages that can be gained by having the robot work with a person to cooperatively perform manipulation tasks. The second theme, task relevant features, emphasizes the benefits of carefully selecting the aspects of the world that are to be perceived and acted upon during a manipulation task. The third theme, let the body do the thinking, encompasses several ways in which a robot can use its body to simplify manipulation tasks. 1 Fig. 1. The humanoid robot Domo used in this paper. I.

The goal of this article is to introduce the reader to the rich and vibrant field of robotics, in hopes of laying out an agenda for future research on human robot interaction. Robotics is a field in change; the meaning of the term “robot ” today differs substantially from the term just a decade ago. The primary purpose of this article is to provide a comprehen-sive description of past and present-day robotics. It identifies the major epochs of robotic technology and systems—from industrial to service robotics—and characterizes the dif-ferent styles of human robot interaction paradigmatic for each epoch. To set an agenda for research on human robot interaction, the article articulates some of the most pressing open questions pertaining to modern-day human robot interaction. 2 1

Human-Robot Interaction (HRI) for socially assistive applications is a growing and increasingly popular research area at the intersection of robotics, health science, psychology, social science, and cognitive science. Assistive robotics has the potential to enhance the quality of life for large populations of users. In response to the rapidly growing elderly population, a great

Abstract- Social robots recognize and respond to human social cues with appropriate behaviors. The capabilities used to build social robots can be uniquely applied to assist in the diagnosis and treatment of autism, a pervasive developmental disorder which results in selective impairment of social abilities. This paper outlines some of the ways in which social robots can provide unique perspectives to address critical problems in diagnosing autism. We provide preliminary data and observations on how this result can be achieved based on three years of immersion in a clinical research group that performs diagnostic evaluations of more than 130 children per year.

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions.

Robots often incorporate computational models of visual attention to streamline processing. Even though the number of visual attention systems employed on robots has increased dramatically in recent years, the evaluation of these systems has remained primarily qualitative and subjective. We introduce quantitative methods for evaluating computational models of visual attention by direct comparison with gaze trajectories acquired from humans. In particular, we focus on the need for metrics based not on distances within the image plane, but that instead operate at the level of underlying features. We present a framework, based on dimensionality-reduction over the features of human gaze trajectories, that can simultaneously be used for both optimizing a particular computational model of visual attention and for evaluating its performance in terms of similarity to human behavior. We use this framework to evaluate the Itti et al. (1998) model of visual attention, a computational model that serves as the basis for many robotic visual attention systems.

This study examines the influence of perceived social abilities of a robot on user’s attitude towards and acceptance of the robot. An interface robot with simulated conversational capabilities was used in a Wizard of Oz experiment with two conditions: a more socially communicative (the robot made use of a larger set of social abilities in interaction) and a less socially communicative interface. Participants (n=40) were observed in 5 minute interaction sessions and were asked to answer questions on perceived social abilities and technology acceptance. Results show that participants who were confronted with the more socially communicative version of the robot felt more comfortable and were more expressive in communicating with it. This suggests that the more socially communicative condition would be more likely to be accepted as a conversational partner. However, the findings did not show a significant correlation between perceived social abilities and technology acceptance.

Microphones and cameras have been extensively used to observe and detect human activity and to facilitate natural modes of interaction between humans and intelligent systems. Human brain processes the audio and video modalities extracting complementary and robust information from them. Intelligent systems with audio-visual sensors should be capable of achieving similar goals. The audio-visual information fusion strategy is a key component in designing such systems. In this paper we exclusively survey the fusion techniques used in various audio-visual information fusion tasks. The fusion strategy used tends to depend mainly on the model, probabilistic or otherwise, used in the particular task to process sensory information to obtain higher level semantic information. The models themselves are task oriented. In this paper we describe the fusion strategies and the corresponding models used in audiovisual tasks such as speech recognition, tracking, biometrics, affective state recognition and meeting scene analysis. We also review the challenges and existing solutions and also unresolved or partially resolved issues in these fields. Specifically, we discuss established and upcoming work in hierarchical fusion strategies and crossmodal learning techniques, identifying these as critical areas of research in the future development of intelligent systems.