  A compact information-rich representation of the environment, also called a
feature abstraction, can simplify a robot's task of mapping its raw sensory
inputs to useful action sequences. However, in environments that are
non-stationary and only partially observable, a single abstraction is probably
not sufficient to encode most variations. Therefore, learning multiple sets of
spatially or temporally local, modular abstractions of the inputs would be
beneficial. How can a robot learn these local abstractions without a teacher?
More specifically, how can it decide from where and when to start learning a
new abstraction? A recently proposed algorithm called Curious Dr. MISFA
addresses this problem. The algorithm is based on two underlying learning
principles called artificial curiosity and slowness. The former is used to make
the robot self-motivated to explore by rewarding itself whenever it makes
progress learning an abstraction; the later is used to update the abstraction
by extracting slowly varying components from raw sensory inputs. Curious Dr.
MISFA's application is, however, limited to discrete domains constrained by a
pre-defined state space and has design limitations that make it unstable in
certain situations. This paper presents a significant improvement that is
applicable to continuous environments, is computationally less expensive,
simpler to use with fewer hyper parameters, and stable in certain
non-stationary environments. We demonstrate the efficacy and stability of our
method in a vision-based robot simulator.
