Exploiting symmetries in reinforcement learning of bimanual robotic tasks
Cita com:
hdl:2117/178327
Tipus de documentArticle
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Movement Primitives (MPs) have been widely adopted for representing and learning robotic movements using Reinforcement Learning Policy Search. Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, able of capturing the variability allowed while executing a movement. This approach has proved effective in learning a wide range of robotic movements, but it comes with the need of dealing with a high-dimensional space of parameters. This may be a critical problem when learning tasks with two robotic manipulators, and this work proposes an approach to reduce the dimension of the parameter space based on the exploitation of symmetry. A symmetrization method for ProMPs is presented and used to represent two movements, employing a single ProMP for the first arm and a symmetry surface that maps that ProMP to the second arm. This symmetric representation is then adopted in reinforcement learning of bimanual tasks (from user-provided demonstrations), using Relative Entropy Policy Search (REPS) algorithm. The symmetry-based approach developed has been tested in an experiment of cloth manipulation, showing a speed increment in learning the task.
Descripció
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CitacióAmadio, F.; Colomé, A.; Torras, C. Exploiting symmetries in reinforcement learning of bimanual robotic tasks. "IEEE robotics and automation letters", 2019, vol. 4, núm. 2, p. 1838-1845.
ISSN2377-3766
Versió de l'editorhttps://ieeexplore.ieee.org/document/8637816
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