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• W3206592106 abstract "Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (RL) setting, especially for learning with physical robots. In this paper we propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics, using mixtures of probabilistic principal component analyzers (MPPCA) on the movements&#x2019; parameter space. Moreover, we introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO). LAMPO can provide gradient estimates from previous experience using self-normalized importance sampling, hence, making full use of samples collected in previous learning iterations. These advantages combined provide a complete framework for sample-efficient off-policy optimization of movement primitives for robot learning of high-dimensional manipulation skills. Our experimental results conducted both in simulation and on a real robot show that LAMPO provides sample-efficient policies against common approaches in literature. Code available at https://github.com/SamuelePolimi/lampo." @default.
• W3206592106 created "2021-10-25" @default.
• W3206592106 creator A5026055366 @default.
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• W3206592106 date "2021-05-30" @default.
• W3206592106 modified "2023-10-13" @default.
• W3206592106 title "Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills" @default.
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• W3206592106 doi "https://doi.org/10.1109/icra48506.2021.9561870" @default.
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