Using social robots to study abnormal social development

Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of abnormal social development. Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism

Exploiting Vestibular Output during Learning Results in Naturally Curved Reaching Trajectories

Teaching a humanoid robot to reach for a visual target is a complex problem in part because of the high dimensionality of the control space. In this paper, we demonstrate a biologically plausible simplification of the reaching process that replaces the degrees of freedom in the neck of the robot with sensory readings from a vestibular system. We show that this simplification introduces errors that are easily overcome by a standard learning algorithm. Furthermore, the errors that are necessarily introduced by this simplification result in reaching trajectories that are curved in the same way as human reaching trajectories

A Binocular, Foveated Active Vision System

This report documents the design and implementation of a binocular, foveated active vision system as part of the Cog project at the MIT Artificial Intelligence Laboratory. The active vision system features a three degree of freedom mechanical platform that supports four color cameras, a motion control system, and a parallel network of digital signal processors for image processing. To demonstrate the capabilities of the system, we present results from four sample visual-motor tasks

That's Mine! Learning Ownership Relations and Norms for Robots

The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) percept-based prediction of an object's likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.Comment: 9 pg., 2 fig., accepted for AAAI-2019. Video demo: https://bit.ly/2z8obET GitHub: https://github.com/OwnageBot/ownage_bo

High-level perceptual contours from a variety of low-level physical features

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 87-90).by Brian M. Scassellati.M.Eng

Effects of form and motion on judgments of social robots' animacy, likability, trustworthiness and unpleasantness

One of robot designers' main goals is to make robots as sociable as possible. Aside from improving robots' actual social functions, a great deal of effort is devoted to making them appear lifelike. This is often achieved by endowing the robot with an anthropomorphic body. However, psychological research on the perception of animacy suggests another crucial factor that might also contribute to attributions of animacy: movement characteristics. In the current study, we investigated how the combination of bodily appearance and movement characteristics of a robot can alter people's attributions of animacy, likability, trustworthiness, and unpleasantness. Participants played games of Tic-Tac-Toe against a robot which (1) either possessed a human form or did not, and (2) either exhibited smooth, lifelike movement or did not. Naturalistic motion was judged to be more animate than mechanical motion, but only when the robot resembled a human form. Naturalistic motion improved likeability regardless of the robot's appearance. Finally, a robot with a human form was rated as more disturbing when it moved naturalistically. Robot designers should be aware that movement characteristics play an important role in promoting robots' apparent animacy.This work was partially supported by the Spanish Government through the project call "Aplicaciones de los robots sociales", DPI2011-26980 from the Spanish Ministry of Economy and Competitiveness. Álvaro Castro-González was partially supported by a grant from Universidad Carlos III de Madrid

Bridging the Research Gap: Making HRI Useful to Individuals with Autism

While there is a rich history of studies involving robots and individuals with autism spectrum disorders (ASD), few of these studies have made substantial impact in the clinical research community. In this paper we first examine how differences in approach, study design, evaluation, and publication practices have hindered uptake of these research results. Based on ten years of collaboration, we suggest a set of design principles that satisfy the needs (both academic and cultural) of both the robotics and clinical autism research communities. Using these principles, we present a study that demonstrates a quantitatively measured improvement in human-human social interaction for children with ASD, effected by interaction with a robot

Robot tool use: A survey

Using human tools can significantly benefit robots in many application domains. Such ability would allow robots to solve problems that they were unable to without tools. However, robot tool use is a challenging task. Tool use was initially considered to be the ability that distinguishes human beings from other animals. We identify three skills required for robot tool use: perception, manipulation, and high-level cognition skills. While both general manipulation tasks and tool use tasks require the same level of perception accuracy, there are unique manipulation and cognition challenges in robot tool use. In this survey, we first define robot tool use. The definition highlighted the skills required for robot tool use. The skills coincide with an affordance model which defined a three-way relation between actions, objects, and effects. We also compile a taxonomy of robot tool use with insights from animal tool use literature. Our definition and taxonomy lay a theoretical foundation for future robot tool use studies and also serve as practical guidelines for robot tool use applications. We first categorize tool use based on the context of the task. The contexts are highly similar for the same task (e.g., cutting) in non-causal tool use, while the contexts for causal tool use are diverse. We further categorize causal tool use based on the task complexity suggested in animal tool use studies into single-manipulation tool use and multiple-manipulation tool use. Single-manipulation tool use are sub-categorized based on tool features and prior experiences of tool use. This type of tool may be considered as building blocks of causal tool use. Multiple-manipulation tool use combines these building blocks in different ways. The different combinations categorize multiple-manipulation tool use. Moreover, we identify different skills required in each sub-type in the taxonomy. We then review previous studies on robot tool use based on the taxonomy and describe how the relations are learned in these studies. We conclude with a discussion of the current applications of robot tool use and open questions to address future robot tool use