Statistical Relational Learning of Object Affordances for Robotic Manipulation

Abstract

We present initial results of an application of statistical relational learning using ProbLog to a robotic manipulation task modeled using affordances. Affordances encompass the action possibilities on an object, so previous works have presented models for just one object. However, in scenarios where there are multiple objects that interact between each other, it is very useful to consider the advantages of the statistical relational learning.