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 provides a brief description of the project goals and design requirements, discusses the problem of autonomy in the context of story-based believable agents, and finally describes an architecture that uses the dramatic beat as a structural principle to integrate plot and character

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

Hierarchical organization has become an unfashionable model of intelligent control within some communities of both natural and artificial intelligence. What has replaced it are models based on parallel distributed processes, both neural and behavior based, or dynamical systems theory, which denies modularity, let alone rigorous structure. In this paper we present experimental results demonstrating an artificial reactive hierarchybased system that outperforms fully parallel systems in a highly dynamic environment with a large number of conflicting goals. This work is conducted in Tyrrell’s (1993) Simulated Environment and can be seen as an extension of his work on comparing action selection mechanisms. We observe that the hierarchical strategy has also been well demonstrated in nature. We argue that, for complex intelligences, preserving full reactivity may not be worth the cost in terms of the complexity of action selection.

This research was funded in part through fellowships from the Litton and Intel Corporations. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect those of the sponsors.

A prototype system for implementing a virtual Per- sonal Aerobics Trainer (PAT) is presented. Unlike workout video tapes or TV exercise shows, this system allows the user to create and personalize an aerobics session to meet the user's needs and desires. Various media technology and computer vision algorithms are used to enhance the interaction of a virtual instructor by enabling it to watch and talk to the user. Throughout the paper, we report the system components and discuss the advantages, problems, and extensions of the design.

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.

We have created an automatic cinematography system for interactive virtual environments. This system controls a virtual camera and lights in a three-dimensional virtual world inhabited by a group of autonomous and user-controlled characters. By dynamically changing the camera and the lights, our system facilitates the interaction of human participants with this world and displays the emotional content of the digital scene. Building on the tradition of cinema, modern video games, and autonomous behavior systems, we have constructed this cinematography system with an ethologically-inspired structure of sensors, emotions, motivations, and action-selection mechanisms. Our system breaks shots into elements, such as which actors the camera should focus on or the angle it should use to watch them. Hierarchically arranged cross-exclusion groups mediate between the various options, arriving at the best shot at each moment in time. Our cinematography system uses the same approach that we use fo...

This paper presents a computational model of real-time task-oriented dialogue skills. The architecture, termed Ymir, bridges between multimodal perception and multimodal action and supports the creation of autonomous computer characters that afford full-duplex, real-time face-to-face interaction with a human. Ymir has been prototyped in software, and a humanoid created, called Gandalf, capable of fluid multimodal dialogue. Ymir demonstrates several new ideas in the creation of communicative computer agents, including perceptual integration of multimodal events, distributed planning and decision making, an explicit handling of real-time, and layered input analysis and motor control with human characteristics. This paper describes the architecture and explains its main elements. Examples ofimplementation and performance are given, and the architectures limitations and possibilities are discussed.

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.