Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification. In the proposed formalization of affordances, actions and effects are related to visual features, not objects, and they can be combined. We have tested the approach on three action primitives and on a real PR2 robot.
According to a recent survey @cite_17 , among 146 reviewed papers, 104 papers consider learning affordances directly from a meso level, i.e. considering objects as a whole, while only 27 papers consider it from global level, i.e. by considering the whole environment and only 15 papers from a local level. With the global level, considering the whole environment allows the learning system to integrate the context. The context is important to predict or to do recognition of high-level affordances. Most papers on affordance use the meso level because for most actions having a complete model of an object is practical. For instance, for successful grasps, the object states such as orientation and position or shape are important information. Learning affordances at a local level allows the system to perceive them directly, which is in line with Gibson's view. Moreover considering the local level is simpler and is thus suitable to bootstrap the system.
@cite_17
2754217830
J. J. Gibson’s concept of affordance, one of the central pillars of ecological psychology, is a truly remarkable idea that provides a concise theory of animal perception predicated on environmental...