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Abstract

Abstraction of complex, longer motor tasks
into simpler elemental movements enables
humans and animals to exhibit motor skills
which have not yet been matched by robots.
Humans intuitively decompose complex motions
into smaller, simpler segments. For
example when describing simple movements
like drawing a triangle with a pen, we can
easily name the basic steps of this movement.
Surprisingly, such abstractions have rarely
been used in artificial motor skill learning algorithms.
These algorithms typically choose
a new action (such as a torque or a force) at a
very fast time-scale. As a result, both policy
and temporal credit assignment problem become
unnecessarily complex - often beyond
the reach of current machine learning methods.
We introduce a new framework for temporal
abstractions in reinforcement learning (RL),
i.e. RL with motion templates. We present a
new algorithm for this framework which can
learn high-quality policies by making only
few abstract decisions.