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

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.

We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and understand the actions of others in intentional terms. Among the key ideas that we draw from work on the development of human social intelligence, the most crucial is the hypothesis that in human infants, imitative interactions, starting with facial mimicry, are a significant stepping-stone in developing appropriate social behavior, learning to predict other’s actions, and ultimately, understanding the intensions of others. 1

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply `pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively.

Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot’s attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot’s learning process. We integrate this perceptual ability into our robot’s ”emotion ” system, thereby allowing a human to directly manipulate the robot’s affective state. This has a powerful organizing influence on the robot’s behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naive subjects.

This paper presents ongoing work towards building an autonomous robot that learns in a social context. The mode of

Newly emerging robotics applications for domestic or entertainment purposes are slowly introducing autonomous robots into society at large. A critical

This paper presents a motivational system for an autonomous robot which is designed to regulate human-robot interaction. The mode of social interaction is that of a caretaker-infant dyad where a human acts as the caretaker for the robot. An infant's emotions and drives play a very important role in generating meaningful interactions with the caretaker, and regulating these interactions to maintain an environment suitable for the learning process (Bullowa 1979). Similarly, the learning task for the robot is to apply various communication skills acquired during social exchanges to manipulate the caretaker such that its drives are satisfied. Toward this goal, the motivational system implements drives, emotions, and facial expressions. Although the details of the learning itself are beyond the scope of this paper, this work represents an important step toward realizing robots that can engage in meaningful bi-directional social interactions with humans.

This paper presents an autonomous robot designed to interact socially with human "parents ". A human infant's emotions and drives play an important role in generating meaningful interactions with the caretaker, regulating these interactions to maintain an environment suitable for the learning process, and assisting the caretaker in satisfying the infant's drives. For our purposes, the ability to regulate how intensely the caretaker engages the robot is vital to successful learning in a social context. To achieve a similar interaction dynamic, we present a general framework that integrates perception, attention, drives, emotions, behavior selection, and motor acts. We then present a specific implementation of this architecture which enables the robot to perceive both salient social stimuli (faces) and salient non-social stimuli (motion). The robot responds with expressive displays which reflect an ever-changing motivational state and which give the human cues on how to satisfy the robot...