TRENDS in Cognitive Sciences Vol.6 No.11 November 2002 http://tics.trends.com 1364-6613/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S1364-6613(02)02016-8 481 Review Cynthia Breazeal The Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Ave NE18-5FL, Cambridge MA 02139, USA. Brian Scassellati Dept of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06520, USA. The study of the mechanisms that enable an individual to acquire information or skills from another individual has been a seminal topic in many areas of cognitive science. For example, ethologists attempt to understand how bees communicate the location of food sources, to describe how successive generations of blue-tits learn to open milk cans, and to categorize the spread of tool use in chimpanzee troops. Developmental psychologists study the emergence of social learning mechanisms in human infants from the very early (but simple) imitative responses of the newborn [1] to the complex replication of task goals that toddlers demonstrate [2]. Research in robotics has focused on social learning for many reasons. Commercial interest in building robots that can be used by ordinary people in their homes, their workplaces, and in public spaces such as hospitals and museums, invoke social learning as a mechanism for allowing users to customize systems to particular environments or user preferences. Research in artificial intelligence has focused on social learning as a possible means for building machines that can acquire new knowledge autonomously, and become increasingly more complex and capable without requiring additional effort from human designers. Other researchers implement models of social behavior in machines to gain a deeper understanding of social learning in animals (including humans). Differences between the study of social learning in animals and machines The methods for studying social learning in artificial systems differ significantly from methods used to study social learning in biological systems. When studying animals, researchers attempt to determine the minimal set of capabilities required to produce an observed behavior. Precise taxonomies of the types of required skill have been developed; however, none of these is universally accepted (see Box 1). Although these descriptions often focus on cognitive skills, they do not completely capture the ways in which these skills can be constructed or combined to produce the observed behavior. Whereas biological studies tend to be descriptive, studies of social learning in artificial systems are primarily generative; researchers attempt to construct a desired behavior from a minimal set of capabilities. These studies often use imprecise definitions of the external behavior (often using the word imitation to mean any type of social learning), but can precisely specify the underlying mechanisms of the system (see Box 2). Although these methodological differences do produce terminology problems between these related disciplines, on the whole, the literature on social learning in animals is a very accessible source of inspiration for robots, both physical and simulated (see Box 3). The study of social learning in robotics has been motivated by both scientific interest in the learning process and practical desires to produce machines that are useful, flexible, and easy to use. In this review, we introduce the social and task-oriented aspects of robot imitation. We focus on methodologies for addressing two fundamental problems. First, how does the robot know w hat to imitate? And second, how does the robot map that perception onto its ow n action repertoire to replicate it? In the future, programming humanoid robots to perform new tasks might be as simple as show ing them. Robots that imitate humans Cynthia Breazeal and Brian Scassellati regional brain activity. J. Sleep Res. 9, 335–352 47 Cajochen, C. et al. (2001) Dynamics of frontal EEG activity, sleepiness and body temperature under high and low sleep pressure. Neuroreport 12, 2277–2281 48 Binks, P.G. et al. (1999) Short-term total sleep deprivation does not selectively impair higher cortical functioning. Sleep 22, 328–334 49 Pace-Schott, E.F. et al. (2002) Healthy young male adults are resistant to sleep-deprivation induced deficits in dorsolateral prefrontal function. Sleep 25 (Suppl.), A446 50 Pace-Schott, E.F. et al. 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