Abstract
We propose situated affordances as an extension to the well known concept of affordances. Situated affordances extend affordances by taking the environmental context, in which an object is embedded, into account. We argue that the extended concept allows the learning of qualitatively more complex tasks. In this paper we report the conceptual result of ongoing work in which a cognitive robotic architecture is developed that finally should be able to learn complex real world tasks. The purpose of this paper is to communicate the proposed conceptual idea to the scientific community, to get in touch with other researchers and to foster the interchange between roboticists, psychologists and associated researchers who are concerned with the learning of complex tasks in robots.
Highlights
How humanoid robots can be enabled to learn complex tasks is still an open question
In this paper we report the conceptual result of ongoing work in which a cognitive robotic architecture is developed that should be able to learn complex real world tasks
We mentioned and explained situated affordances in the context of complex task learning, as this is the domain in which robotic affordance experiments are normally conducted and it is the domain in which we are applying it
Summary
How humanoid robots can be enabled to learn complex tasks is still an open question. The concept of affordances became a popular method in robotic research. The term affordances was defined by the psychologist Gibson [1]. It describes action opportunities an observer becomes aware of by looking at an environment or at an object. A pen affords to write and a ball affords to kick. The adoption of the concept affordances in robotics research allowed the tackling of versatile and complex learning tasks
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