  This work presents an architecture that generates curiosity-driven
goal-directed exploration behaviours for an image sensor of a microfarming
robot. A combination of deep neural networks for offline unsupervised learning
of low-dimensional features from images, and of online learning of shallow
neural networks representing the inverse and forward kinematics of the system
have been used. The artificial curiosity system assigns interest values to a
set of pre-defined goals, and drives the exploration towards those that are
expected to maximise the learning progress. We propose the integration of an
episodic memory in intrinsic motivation systems to face catastrophic forgetting
issues, typically experienced when performing online updates of artificial
neural networks. Our results show that adopting an episodic memory system not
only prevents the computational models from quickly forgetting knowledge that
has been previously acquired, but also provides new avenues for modulating the
balance between plasticity and stability of the models.
