Titlebook: Reinforcement Learning of Bimanual Robot Skills; Adrià Colomé,Carme Torras Book 2020 Springer Nature Switzerland AG 2020 Robotics.Artifici

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Dimensionality Reduction with Movement Primitivesnearity in the parameters, rescaling robustness and continuity. However, when learning a movement with MPs, a very large number of Gaussian approximations needs to be performed. Adding them up for all joints yields too many parameters to be explored when using Reinforcement Learning (RL), thus requi

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Preliminariesve review, with a detailed description of many of the state-of-the-art PS algorithms and the reasoning behind them. We will also introduce the concept of Movement Primitives in Sect. ., a motion characterization very suitable to PS.

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Generating and Adapting Probabilistic Movement Primitivesh contextual PS. This method is combined with a stochastic-based obstacle avoidance that allows to modify these trajectories. Moreover, this chapter presents generative mixture models of ProMPs, which can be built from data that may include different actions.

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1610-7438 ns are proposed...In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot moti978-3-030-26328-7978-3-030-26326-3Series ISSN 1610-7438 Series E-ISSN 1610-742X