Humanoid Robot Control by Offline Actor-Critic Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F17%3A00313777" target="_blank" >RIV/68407700:21240/17:00313777 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-1885/71.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/71.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Humanoid Robot Control by Offline Actor-Critic Learning

Original language description

In this paper, we present our results on application of reinforcement learning on full body control of a humanoid robot. The task we try to learn is achieving vertical position of robot’s torso from an initial position of laying flat on the ground. Our experimental setup includes an instance of the NAO robot in theWebots simulation environment. We use an actor-critic neural agent. As this is a work in progress, we only train offline from a sample of random movements. We present a series of experiments on a simplified task and a final evaluation on the humanoid robot control task that shows improvement over random policy.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů