Comparison of action-grounded and non-action-grounded 3-D shape features for object affordance classification

Recent work in robotics, particularly in the domains of object manipulation and affordance learning, has seen the development of action-grounded features, that is, object features that are defined dynamically with respect to manipulation actions. Rather than using pose-invariant features, as is often the case with object recognition, such features are grounded with respect to the manipulation of the object, for instance, by using shape features that describe the surface of an object relative to the push contact point and direction. In this paper we provide an experimental comparison between action-grounded features and non-grounded features in an object affordance classification setting. Using an experimental platform that gathers 3-D data from the Kinect RGB-D sensor, as well as push action trajectories from an electromagnetic tracking system, we provide experimental results that demonstrate the effectiveness of this action-grounded approach across a range of state-of-the-art classifiers.