Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization
This addresses how motor learning patterns might arise from optimization principles, with implications for developmental robotics and AI, though it is incremental in modeling known biological phenomena.
The study investigated whether proximodistal exploration in motor learning, as seen in infants, can emerge from stochastic optimization without innate schedules, and found that it appears spontaneously in simulated arm reaching tasks across different morphologies.
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.}