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.

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In this paper I claim that one of the main characteristics that makes the Evolutionary Robotics approach suitable for the study of adaptive behavior in natural and artificial agents is the possibility to rely largely on a self-organization process. Indeed by using Artificial Evolution the role of the designer may be limited to the specification of a fitness function which measures the ability of a given robot to perform a desired task. From an engineering point of view the main advantage of relying on self-organization is the fact that the designer does not need to divide the desired behavior into simple basic behaviors to be implemented into separate layers (or modules) of the robot control system. By selecting individuals for their ability to perform the desired behavior as a whole, simple basic behaviors can emerge from the interaction between several processes in the control system and from the interaction between the robot and the environment. From the point of view of ...

An agent in the real world has to be able to make distinctions between different types of objects, i.e. it must have the competence of categorization. In mobile agents categorization is hard to achieve because there is a large variation in proximal sensory stimulation originating from the same object. In this paper we extend previous work on adaptive categorization in autonomous agents. The main idea of our approach is to include the agent's own actions into the classification process. In the experiments presented in this paper an agent equipped with an active vision and an arm-gripper system has to collect certain types of objects. The agent learns about the objects by actively exploring them. This exploration results in visual and haptic information that is used for learning. In essence, the categorization comes about via evolving reentrant connections between the haptic and the visual system. Results on the behavioral performance as well as the underlying internal dynamics are prese...

In this paper, we propose an unsupervised neural network allowing a robot to learn sensory-motor associations with a delayed reward. The robot task is to learn the "meaning" of pictograms in order to "survive" in a maze. First, we introduce a new neural conditioning rule (PCR: Probabilistic Conditioning Rule) allowing to test hypotheses (associations between visual categories and movements) during a given time span. Afterwards, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kinds of mazes. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition. Keywords: Neural Networks, Unsupervised Learning, Topological Maps...

. Is it more efficient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a specific task, namely learning the topography of an environment whose features change frequently. We propose a theoretical framework based on probabilistic modeling to describe the system's dynamics. The adaptive multirobot system and its dynamic environment are modeled though a set of probabilistic equations. The model gives an explicit description of the influence of the variables of the system, namely the number of worker robots, the frequency of environmental changes and the environment's configuration, on the data collecting performance of the group. It is then used to determine boundaries for these system's variables within which the learning is succ...

This paper addresses the problem of automatic skill acquisition by a robot. It reports that six trials of a reach-grasp-release-retract skill are sufficient for learning a canonical description of the task under the following circumstances: The robot is Robonaut, NASA's space-capable, dexterous humanoid. Robonaut was teleoperated by a person using full immersion Virtual Reality technology that transforms the operator's arm and hand motions into those of the robot. The operator 's sole source of real-time feedback was visual. During the six trials all of the Robot's sensory inputs and motor control parameters were recorded as time-series. Later the time-series from each trial was partitioned into the same number of episodes as a function of changes in the motor parameter sequence. The episodes were time normalized and averaged across trials The resultant motor parameter sequence and sensor signals were used to control the robot without the teleoperator. The robot was able to perform the task autonomously with robot starting positions and object locations both similar to, and different from the original trials.

Developmental robotics is an emerging field located at the intersection of developmental psychology and robotics, that has lately attracted quite some attention. This paper gives a survey of a variety of research projects dealing with or inspired by developmental issues, and outlines possible future directions.

The software control system for ISAC is described. ISAC is a humanoid robot designed and built by researchers at the Intelligent Robotics Laboratory in the Center for Intelligent Systems at Vanderbilt University. The robot was designed for close human interaction with the goal of enabling it to become intel ligent. Possible requirements for intelligence in a robot are presented with reasons for making human-robot interaction a fundamental design constraint. The agentbased, parallel, distributed software architecture used to construct the control system is described. The types of agents and their interactions are described. An approach for using these tools to enable ISAC to learn from its own experiences under the tutelage of a human instructor is proposed. 1 Introduction At the Intelligent Robotics Laboratory (IRL) of the Center for Intelligent Systems at Vanderbilt University we have been developing a humanoid robot, ISAC, over the past several years (Fig. 1). The robot was designe...

. One important implication of embodiment is that, by acting, agents partially determine the sensory patterns they receive from the environment. The motor actions performed by an agent, by modifying the agent's position with respect to the external environment and/or the external environment itself, partially determine the type of sensory patterns received from the environment. In this paper we investigate how agents can take advantage of this ability. In particular, we discuss how agents coordinate sensory and motor processes in order to (1) select sensory patterns which are not affected by the aliasing problem and avoid those which are; (2) select sensory patterns such that groups of patterns which require different responses do not strongly overlap; (3) exploit emergent behaviors that result from the interaction between the agent and the environment. 1. Introduction Recently, a new research paradigm has challenged the traditional view according to which intelligence is ...