This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit

A new approach to object recognition is presented, in which secondary representations of 3-D models are synthesized/derived (in various forms) and subsequently compared in order to invoke views of models, tune model pose and verify recognition hypotheses. The use of these secondary representations allows complex models (e.g. surface-based or volumetric models) to be compared implicitly (rather than explicitly comparing the component primitives of the models). This in turn overcomes the problem of the stability of the model primitives, and provides independence between the complex 3-D representations and the recognition strategy (i.e. the invocation, matching and verification techniques). The secondary representations employed are Extended Gaussian Images, directional histograms, needle diagrams, depth maps and boundary curvature signatures. The technique is demonstrated using models, of reasonably complex objects, derived from actively sensed range data.

This paper describes experience gained in using optical flow fields, arising from constrained camera motion, to estimate range information, for robotic part manipulation. Several key issues in robot vision are addressed. These include the computation of optical flow, computation of depth, interpolation of depth values, model construction, object recognition, and pose estimation. The paper provides an example of the approach and evaluates the system's performance in the context of its applicability to part manipulation.>

A technique is described for determining a depth map of parts in bins using optical flow derived from camera motion. Simple programmed camera motions are generated by mounting the camera on the robot end effector and directing the effector along a known path. The results achieved using two simple trajectories, where one is along the optical axis and the other is in rotation about a fixation point, are detailed. Optical flow is estimated by computing the time derivative of a sequence of images, i.e. by forming differences between two successive images and, in particular, matching between contours in images that have been generated from the zero crossings of Laplacian of Gaussian-filtered images. Once the flow field has been determined, a depth map is computed utilizing the parameters of the known camera trajectory. Empirical results are presented for a calibration object and two bins of parts; these are compared with the theoretical precision of the technique, and it is demonstrated that a ranging accuracy on the order of two parts in 100 is achievable.>

Abstract Image understanding is concerned with the elucidation of a computational base inherent in perceiving a three‐dimensional world using vision. This paper describes a low‐level (or early) vision software system, developed in the context of current collaborative research activities in image understanding, which goes some way toward fulfilling the goals of portability, ease of use, and general‐purpose extensibility. Since visual perception uses several types of disparate, but interrelated, information in some explicit cognitive organization, a central objective of the work is to represent this information in a coherent integrated manner which allows one interactively to investigate the properties of the interdependency between information types.