  When an autonomous robot learns how to execute actions, it is of interest to
know if and when the execution policy can be generalised to variations of the
learning scenarios. This can inform the robot about the necessity of additional
learning, as using incomplete or unsuitable policies can lead to execution
failures. Generalisation is particularly relevant when a robot has to deal with
a large variety of objects and in different contexts. In this paper, we propose
and analyse a strategy for generalising parameterised execution models of
manipulation actions over different objects based on an object ontology. In
particular, a robot transfers a known execution model to objects of related
classes according to the ontology, but only if there is no other evidence that
the model may be unsuitable. This allows using ontological knowledge as prior
information that is then refined by the robot's own experiences. We verify our
algorithm for two actions - grasping and stowing everyday objects - such that
we show that the robot can deduce cases in which an existing policy can
generalise to other objects and when additional execution knowledge has to be
acquired.
