INTRODUCTION Classical symbolic systems in psychology have proven to be powerful in explaining human cognition and behavior, especially in contrast to the preceding behaviorist approach. The engineering aspect of this approach, symbolic artificial intelligence, has similarly has shown itself to be capable of mimicking and often exceeding average human cognitive abilities. However, both the cognitive science and AI communities have experienced an increasing dissatisfaction with these approaches within the last decade or so. The problems manifest in both fields are similar. The things humans do with the most ease (e.g., recognizing patterns, moving around in the world, learning to speak) are the most difficult to explain and mimic with a symbolic approach. In both domains, this dissatisfaction has led to the acceptance of different viewpoints. In cognitive science, connectionism and ecological psychology are having major influences, and in AI, specifically the fields of robotics and comp

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There are striking parallels between ecological psychology and the new trends in robotics and computer vision, particularly regarding how agents interact with the environment. We present some ideas from ecological psychology, including control laws using optic flow, affordances and action modes, and describe our implementation of these concepts in a small mobile robot which can avoid obstacles and chase or flee moving targets solely using optic flow. The properties of these methods were further explored in simulation. This work ties in with that of others arguing for a methodological approach in robotics which foregoes a central model/planner. Ecological psychology may not only contribute to robotics, but robotic implementations may in turn provide a test bed for ecological principles and a source of ideas which could be tested in animals and humans. Keywords: ecological psychology, behavior-based robotics, optic flow, obstacle avoidance, tag. ECOLOGICAL ROBOTICS 2 Introduction Over...

. We present a theory for the construction of an optimal matched #lter for self-motion induced optic #ow #elds. The matched #lter extracts local #ow components along a set of pre-de#ned directions and weights them according to an optimization principle which minimizes the di#erence between estimated and real egomotion parameters. In contrast to previous approaches, prior knowledge about distance and translation statistics is incorporated in the form of a #world model". Simulations indicate that the matched #lter model yields reliable self-motion estimates. A comparison of the weight distribution used in the model with the local motion sensitivities of individual and small groups of interneurons in the #y visual system shows a close correspondence. This suggests that these so-called tangential neurons are tuned to optic #ow #elds induced by rotation or translation along a particular axis. They seem to weight the local optic #ow according to the contribution of input noise and the expect...

Collision-avoidance and landing responses are mediated by separate pathways in

Binocular interactions underlying the classic optomotor responses of flying flies