Title: Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization,
Abstract: To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body to those that are more distant. Here, we formulate and
study computationally the hypothesis that such patterns can emerge
spontaneously as the result of a family of stochastic optimization processes
(evolution strategies with covariance-matrix adaptation), without an innate
encoding of a maturational schedule. In particular, we present simulated
experiments with an arm where a computational learner progressively acquires
reaching skills through adaptive exploration, and we show that a proximodistal
organization appears spontaneously, which we denote PDFF (ProximoDistal
Freezing and Freeing of degrees of freedom). We also compare this emergent
organization between different arm morphologies -- from human-like to quite
unnatural ones -- to study the effect of different kinematic structures on the
emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
stochastic optimization; modelling; evolution strategies; cross-entropy
methods; policy search; morphology.}