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

How people talk when teaching a robot

We examine affective vocalizations provided by human teach-ers to robotic learners. In unscripted one-on-one interac-tions, participants provided vocal input to a robotic dinosaur as the robot selected toy buildings to knock down. We find that (1) people vary their vocal input depending on the learner’s performance history, (2) people do not wait until a robotic learner completes an action before they provide in-put and (3) people näıvely and spontaneously use intensely affective vocalizations. Our findings suggest modifications may be needed to traditional machine learning models to better fit observed human tendencies. Our observations of human behavior contradict the popular assumptions made by machine learning algorithms (in particular, reinforcement learning) that the reward function is stationary and path-independent for social learning interactions. We also propose an interaction taxonomy that describes three phases of a human-teacher’s vocalizations: direction, spoken before an action is taken; guidance, spoken as the learner communicates an intended action; and feedback, spo-ken in response to a completed action