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2006
IEEE

Learning Predictive Features in Affordance based Robotic Perception Systems

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Learning Predictive Features in Affordance based Robotic Perception Systems
This work is about the relevance of Gibson’s concept of affordances [1] for visual perception in interactive and autonomous robotic systems. In extension to existing functional views on visual feature representations [9], we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. We investigate how the originally defined representational concept for the perception of affordances - in terms of using either optical flow or heuristically determined 3D features of perceptual entities - should be generalized to using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, using both 3D and 2D information. In addition, we emphasize a new framework for cueing and recognition of affordance-like visual entities that could play an important role in future robot control architectures. We argue that affordancelike perce...
Gerald Fritz, Lucas Paletta, Ralph Breithaupt, Eri
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where IROS
Authors Gerald Fritz, Lucas Paletta, Ralph Breithaupt, Erich Rome, Georg Dorffner
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