Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills

• URL: http://arxiv.org/abs/2010.13766v3
• Date: Fri, 11 Feb 2022 01:49:11 GMT
• Title: Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
• Authors: Samuele Tosatto, Georgia Chalvatzaki, Jan Peters
• Abstract summary: We propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics. We introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO) LAMPO provides sample-efficient policies against common approaches in literature.
• Score: 41.140532647789456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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' 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.

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