  The ability to learn a model is essential for the success of autonomous
agents. Unfortunately, learning a model is difficult in partially observable
environments, where latent environmental factors influence what the agent
observes. In the absence of a supervisory training signal, autonomous agents
therefore require a mechanism to autonomously discover these environmental
factors, or sensorimotor contexts.
  This paper presents a method to discover sensorimotor contexts in partially
observable environments, by constructing a hierarchical transition model. The
method is evaluated in a simulation experiment, in which a robot learns that
different rooms are characterized by different objects that are found in them.
