Learning Motor Primitives with Reinforcement Learning
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One of the major challenges in action generation for robotics and in the
understanding of human motor control is to learn the "building blocks of move-
ment generation," or more precisely, motor primitives. Recently, Ijspeert et al.
[1, 2] suggested a novel framework how to use nonlinear dynamical systems as
motor primitives. While a lot of progress has been made in teaching these mo-
tor primitives using supervised or imitation learning, the self-improvement by
interaction of the system with the environment remains a challenging problem.
In this poster, we evaluate different reinforcement learning approaches can be
used in order to improve the performance of motor primitives. For pursuing this
goal, we highlight the difficulties with current reinforcement learning methods,
and line out how these lead to a novel algorithm which is based on natural policy
gradients [3]. We compare this algorithm to previous reinforcement learning
algorithms in the context of dynamic motor primitive learning, and show that
it outperforms these by at least an order of magnitude.
We demonstrate the efficiency of the resulting reinforcement learning method
for creating complex behaviors for automous robotics. The studied behaviors
will include both discrete, finite tasks such as baseball swings, as well as complex
rhythmic patterns as they occur in biped locomotion.