Building an Affordances Map with Interactive Perception
This work addresses the challenge of adaptive perception for robots in unstructured settings, though it is incremental as it builds on existing interactive perception methods.
The paper tackles the problem of enabling robots to learn visual scene understanding through interaction in open environments, resulting in the development of an affordances map approach that was tested on a real PR2 robot with three action primitives.
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.