Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization

International audienceTo 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 proximodis-tally, 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. Research highlights. • We propose a general, domain-independent hypothesis for the developmental organization of freezing and freeing of degrees of freedom observed both in infant development and adult skill acquisition, such as proximo-distal exploration in learning to reach

The Interaction of Maturational Constraints and Intrinsic Motivations in Active Motor Development

Abstract—This paper studies computational models of the coupling of intrinsic motivations and physiological maturational constraints, and argues that both mechanisms may have complex bidirectional interactions allowing the active control of the growth of complexity in motor development which directs an efficient learning and exploration process. First, we outline the Self-Adaptive Goal Generation- Robust Intelligent Adaptive Curiosity algorithm (SAGG-RIAC) that instantiates an intrinsically motivated goal exploration mechanism for motor learning of inverse models. Then, we introduce a functional model of maturational constraints inspired by the myelination process in humans, and show how it can be coupled with the SAGG-RIAC algorithm, forming a new system called McSAGG-RIAC 2. We then present experiments to evaluate qualitative and, more importantly, quantitative properties of these systems when applied to a 12DO