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.

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.

Computer vision is an important and maturing engineering science. It underpins an increasing variety of applications that require the acquisition, analysis, and interpretation of visual information. However, despite recent success in such areas as computational projective geometry [1,2] and appearance-based recognition [3], contemporary computer vision is still a relatively brittle technology. Consequently, its successful exploitation has been limited to relatively narrow application domains such as machine vision for industrial inspection, the analysis of video data for remote monitoring, and the creation of special effects in the film industry. The focus of much recent research has been on finding ways to reduce this brittleness. The term cognitive vision has been introduced in the past few of years to encapsulate an attempt to achieve more robust, resilient, and adaptable computer vision systems by endowing them with a cognitive faculty: the ability to learn, adapt, weigh alternative solutions, and develop new strategies for analysis and interpretation. The key characteristic of a cognitive vision system is its capacity to exhibit robust performance even in circumstances that were not foreseen when it was designed [4]. Furthermore, a cognitive vision system should be able to anticipate events and adapt its operation accordingly. Ideally, a cognitive

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