  To successfully tackle challenging manipulation tasks, autonomous agents must
learn a diverse set of skills and how to combine them. Recently,
self-supervised agents that set their own abstract goals by exploiting the
discovered structure in the environment were shown to perform well on many
different tasks. In particular, some of them were applied to learn basic
manipulation skills in compositional multi-object environments. However, these
methods learn skills without taking the dependencies between objects into
account. Thus, the learned skills are difficult to combine in realistic
environments. We propose a novel self-supervised agent that estimates relations
between environment components and uses them to independently control different
parts of the environment state. In addition, the estimated relations between
objects can be used to decompose a complex goal into a compatible sequence of
subgoals. We show that, by using this framework, an agent can efficiently and
automatically learn manipulation tasks in multi-object environments with
different relations between objects.

Machine Learning;Robotics