Abstract. To explore issues of developmental structure, physical embodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sensory systems, including visual, auditory, vestibular, kinesthetic, and tactile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit tracking, saccades, binocular vergence, and vestibular-ocular and opto-kinetic reflexes), orientation behaviors, motor control techniques, and social behaviors (pointing to a visual target, recognizing joint attention through face and eye finding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied system. 1

This paper presents part of an on-going project to integrate perception, attention, drives, emotions, behavior arbitration, and expressive acts for a robot designed to interact socially with humans. We present the design of a visual attention system based on a model of human visual search behavior from Wolfe (1994). The attention system integrates perceptions (motion detection, color saliency, and face popouts) with habituation e#ects and influences from the robot's motivational and behavioral state to create a context-dependent attention activation map. This activation map is used to direct eye movements and to satiate the drives of the motivational system.

If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set of skills has often been called a “theory of mind.” This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. I further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models.

Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

This paper focuses on the role of emotion and expressive behavior in regulating social interaction between humans and expressive anthropomorphic robots, either in communicative or teaching scenarios. We present the scientific basis underlying our humanoid robot's emotion models and expressive behavior, and then show how these scientific viewpoints have been adapted to the current implementation. Our robot is also able to recognize affective intent through tone of voice, the implementation of which is inspired by the scientific findings of the developmental psycholinguistics community. We first evaluate the robot's expressive displays in isolation. Next, we evaluate the robot's overall emotive behavior (i.e. the coordination of the affective recognition system, the emotion and motivation systems, and the expression system) as it socially engages nave human subjects face-to-face.

In order to interact socially with a human, a robot must convey intentionality, that is, the human must believe that the robot has beliefs, desires, and intentions. We have constructed a robot which exploits natural human social tendencies to convey intentionality through motor actions and facial expressions. We present results on the integration of perception, attention, motivation, behavior, and motor systems which allow the robot to engage in infant-like interactions with a human caregiver. 1

This paper addresses the role of imitation as a means to enhance the learning of communication skills in autonomous robots. A series of robotic experiments are presented in which autonomous mobile robots are taught a synthetic proto-language. Learning of the language occurs through an imitative scenario where the robot replicates the teacher's movements. Imitation is here an implicit attentional mechanism which allows the robot imitator to share a similar set of proprio- and exteroceptions with the teacher. The robot grounds its understanding of the teacher's words, which describe the teacher's current observations, upon its own perceptions which are similar to those of the teacher. Learning of the robot is based on a dynamical recurrent associative memory architecture (DRAMA). Learning is unsupervised and results from the self-organization of the robot's connectionist architecture. Results show that the imitative behavior greatly improves the efficiency and speed of the learning. More...

The evaluation of models of social and behavioral development is difficult in natural settings; ethical concerns, difficulties in implementing experimental procedures, and difficulties in isolating hypothesized variables make experimental evidence difficult or impossible to obtain. We propose the use of human-like robots as a testbed for the evaluation of models of human social development. Robotic implementation of human social models allows for unique opportunities to evaluate those models. In this paper, we review some of the implications of this proposal by examining a case study of an on-going project to implement an existing model of one aspect human social development, the development of joint attention behaviors.

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Imitation is a powerful mechanism whereby knowledge may be transferred between agents (both biological and artificial). Key problems on the topic of imitation have emerged in various areas close to artificial intelligence, including the cognitive and social sciences, animal behavior, robotics, human--computer interaction, embodied intelligence, software engineering, programming by example and machine learning. Artificial systems used to study imitation can both test models of imitation derived from observational or neurobiological data on imitation in animals and then apply them to different kinds of nonbiological systems ranging from robots to software agents. A crucial problem in imitation is the correspondence problem, mapping action sequences of the demonstrator and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes a new general imitation mechanism called Action Learning for Imitation via Correspondence between embodiments (ALICE) that specifically addresses the correspondence problem. The mechanism is implemented and its efficacy illustrated on the "chessworld" testbed that was created to study imitation from an agent-based perspective, i.e., by a particular agent in a particular environment.