Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning

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An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “[35] proposed a model that considers the case”
Incremental learning of full body motion primitives and their sequencing through human motion observation (2012, methodology): “Many different approaches for representing motion primitives have been proposed, including neural networks (Ogata et al. 2005; Erlhagen et al. 2006; Ito and Tani 2004; Andry et al. 2004), stochastic models such as hidden Markov models (HMMs) (Inamura et al. 2004; Takano et al. 2006, 2005; Billard…”
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains (2008, methodology): “Several different techniques have been applied to the modeling the motion primitives, including dynamical models (Nakanishi et al. 2004), neural networks (Ogata et al. 2005), and stochastic models (Inamura et al. 2004 Billard et al. 2006 Taylor et al. 2006).”