The paper addresses the question how a group of physically embodied robotic agents may originate meaning and language through adaptive language games. The main principles underlying the approach are sketched as well as the steps needed to implement these principles on physical agents. Some experimental results based on this implementation are presented. 1 Introduction In the past five years, a large number of robotic agents, i.e. physical systems capable of sensori-motor control, have been built in order to investigate a bottom-up approach to artificial intelligence (see the overview in [8]). Important results have been achieved, particularly by using behavior-oriented architectures [14] and learning methods based on neural networks [6] or genetic algorithms [3]. Nevertheless, it is still largely an open question how these robots may reach sufficient complexity in order to qualify as cognitive agents. Most of the experiments have focused on `low level' tasks like obstacle avoidance o...

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.

The authors implemented a system which performs a fundamental visuomotor coordination task on the humanoid robot Cog. Cog's task was to saccade its pair of two degree-offreedom eyes to foveate on a target, and then to maneuver its six degree-of-freedom compliant arm to point at that target. This task requires systems for learning to saccade to visual targets, generating smooth arm trajectories, locating the arm in the visual field, and learning the map between gaze direction and correct pointing configuration of the arm. All learning was self-supervised solely by visual feedback. The task was accomplished by many parallel processes running on a seven processor, extensible architecture, MIMD computer. 1 Introduction This paper is one of a series of developmental snapshots from the Cog Project at the MIT Artificial Intelligence Laboratory. Cog is a humanoid robot designed to explore a wide variety of problems in artificial intelligence and cognitive science (Brooks & Stein 1994). To da...

Active cameras provide a navigating vehicle with the ability to fixate and track features over extended periods of time, and wide fields of view. While it is relatively straightforward to apply fixating vision to tactical, short-term navigation tasks, using serial fixation on a succession of features to provide global information for strategic navigation is more involved. However, active vision is seemingly well-suited to this task: the ability to measure features over such a wide range means that the same ones can be used as a robot makes a wide range of movements. This has advantages for map-building and localisation. The core work of this thesis concerns simultaneous localisation and map-building for a robot with a stereo active head, operating in an unknown environment and using point features in the world as visual landmarks. Importance has been attached to producing maps which are useful for extended periods of navigation. Many map-building methods fail on extended runs because ...

In this paper we present an approach to robot arm control based on exploiting the dynamical properties of a simple neural network oscillator circuit coupled to the joints of an arm. The entrainment and input/output properties of the oscillators are used to perform a variety of tasks with the same architecture, without any modeling of the arm or its environment. The approach is implemented on two real robot arms, and has been used to tune into the resonant frequency of pendulums, perform multi-joint coordinated motion by turning cranks, and exploit the dynamics of a `Slinky' toy to coordinate the motion of two arms. By exploiting the coupling between the physical arm and the neural oscillator, a range of complex behaviors can be achieved with a very simple system. Keywords: Oscillator, Neural control, Neural network, Robot Manipulator, Rhythmic movement. Neural Control of Rhythmic Arm Movements 2 1 Introduction This paper describes the properties of a set of simple neural network os...

Abstract. Adults are extremely adept at recognizing social cues, such as eye direction or pointing gestures, that establish the basis of joint attention. These skills serve as the developmental basis for more complex forms of metaphor and analogy by allowing an infant to ground shared experiences and by assisting in the development of more complex communication skills. In this chapter, we review some of the evidence for the developmental course of these joint attention skills from developmental psychology, from disorders of social development such as autism, and from the evolutionary development of these social skills. We also describe an on-going research program aimed at testing existing models of joint attention development by building a human-like robot which communicates naturally with humans using joint attention. Our group has constructed an upper-torso humanoid robot, called Cog, in part to investigate how to build intelligent robotic systems by following a developmental progression of skills similar to that observed in human development. Just as a child learns social skills and conventions through interactions with its parents, our robot will learn to interact with people using natural social communication. We further consider the critical role that imitation plays in bootstrapping a system from simple visual behaviors to more complex social skills. We will present data from a face and eye finding system that serves as the basis of this developmental chain, and an example of how this system can imitate the head movements of an individual. 1

We present a novel methodology for building humanlike artificially intelligent systems. We take as a model the only existing systems which are universally accepted as intelligent: humans. We emphasize building intelligent systems which are not masters of a single domain, but, like humans, are adept at performing a variety of complex tasks in the real world. Using evidence from cognitive science and neuroscience, we suggest four alternative essences of intelligence to those held by classical AI. These are the parallel themes of development, social interaction, embodiment, and integration. Following a methodology based on these themes, we have built a physical humanoid robot. In this paper we present our methodology and the insights it affords for facilitating learning, simplifying the computation underlying rich behavior, and building systems that can scale to more complex tasks in more challenging environments.

Both direct, and evolved, behavior-based approaches to mobile robots have yielded a numberofinteresting demonstrations of robots that navigate, map, plan and operate in the real world. The work can best be described as attempts to emulate insect level locomotion and navigation, with very little work on behavior-based non-trivial manipulation of the world. There have been some behavior-based attempts at exploring social interactions, but these too have been modeled after the sorts of social interactions we see in insects. But thinking how to scale from all this insect level work to full human level intelligence and social interactions leads to a synthesis that is very di erent from that imagined in traditional Arti cial Intelligence and Cognitive Science. We report on work towards that goal.

We describe a set of design principles for building "Fungus Eaters". "Fungus Eaters" are complete autonomous systems. The goal is to extract and describe in a compact way a large part of the insights which have been acquired in the animats field. The principles have been developed from a cognitive science perspective. Although they represent only a very modest beginning, they make immediately clear what sort of ideas about intelligence and cognition they endorse. They all contrast sharply with classical thinking. Moreover, they provide powerful heuristics for design. 1 Introduction In their review paper of the first SAB conference in 1990, Jean-Arcady Meyer and Agnès Guillot argue that the animat approach will play an important role in resolving some of the fundamental controversies in the study of intelligence or cognition (Meyer and Guillot, 1991). Four years later, at the third SAB conference, they propose three types of goals for animat research, short term, intermediate term, and ...