Work in human infancy and behavior-based robotics that grounds intelligent abilities in sensorimotor exchanges between a system and its environment shares recurrent problems of when, whether, and how scaling up from basic to supposedly higher abilities is possible. An action-based model of the infant is introduced that converges with features of independently motivated animat models exploiting emergent functionality and challenges alternatives that invoke conceptual representations. Adaptive change routinely exhibited in infants' everyday activities outstrips the scaling-up potential of current robotic systems and clarifies effective principles obeyed by naturally intelligent systems. A general form is outlined to subject-environment interaction that "engineers" restructuring of early abilities in the direction of greater anticipation (considered an upper boundary for the competence of concept-free human and animat systems); and an action-based account of the phenomena is provided. This emphasizes the relationship between representation and situated inference and the role of reciprocal constraints between cognitive and physical-motor mechanisms. Finally, this article questions how far typical self organizing connectionist networks take us toward understanding a system that is capable of mapping recurrent viable patterns of activity into more permanent adaptive changes. , .~, Traditionally, sensorimotor abilities have been of relatively little concern to the mainstream of either artificial intelligence (AI) or developmental psychology. AI's predominantly centralized view of mind has (ab)used the in-principle separation of virtual and physical machines to license preoccupation with disembodied and disembedded programmed models that rely on the manipulation of concepts and of explicit, exhaustive representations to know the world and preplan behavior within it. Developmental psychology has generally allocated sensorimotor abilities to the

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Neither`design' nor`evolutionary' approaches to building behavior-based robots feature a role for development in the genesis of behavioral organization. However, the new Cog Project aims to build a humanoid robot that will display behavioral abilities observed in human infants and proposes making use of ideas from evolution and developmental psychology in its design. This paper o ers a provisional evaluation of this work from a developmental perspective, to show how d e v elopmental study may o er not only a source of phenomena for modelling but also a method that contributes to our understanding of how self-organization works. The design methodology that underlies Cog confronts problems with selection and interpretation of component b e h a viors, and how these may be better understood through appropriate developmental study is illustrated. Principles that underlie the design of Cog are shown to exhibit interesting convergences with infant mechanisms, based on the signi cance of emergent functionality and the action-as opposed to representation-based nature of both initial and outcome mechanisms. However, analysis of infants yields a more constructive v i e w of ability, associated with di erent assumptions about the subject's relationship with the environment. Routes to Understanding Autonomous AgentsArti cial Intelligence's new behavior-based robotics is uni ed by c o m m i t m ent to understanding intelligent systems in terms of speci cs of their physical embodiment, their sensorimotor coupling with the environment, and the organizational possibilities of the situatedness to which these properties give rise. There is less agreement as to whether

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