基于利润分享的双足步行机器人在pomdp环境下的避跌策略研究

2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) Pub Date : 2014-07-01 DOI:10.1109/NaBIC.2014.6921875

Toshihiro Suzuki, Y. Osana

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引用次数: 0

摘要

本文采用可学习确定性策略的利润共享算法，实现了双足步行机器人在pomdp环境下的避跌。在本研究中，采用了可以学习确定性策略的利润分享方法，该方法可以利用观测历史来获得确定性策略。利用双足步行机器人进行了一系列实验，证实了在pomdp环境下，利润共享算法可以学习确定性策略，从而实现姿态控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Fall avoidance of bipedalwalking robot by profit sharing that can learn deterministic policy for POMDPs environments

In this paper, fall avoidance of bipedal walking robot is realized by the Profit Sharing that can learn deterministic policy for POMDPs environments. In this research, the Profit Sharing that can learn deterministic policy for POMDPs environments which can obtain the deterministic policy by using the history of observations is employed. We carried out a series of experiments using bipedal walking robot, and confirmed that attitude control can be realized by the Profit Sharing that can learn deterministic policy for POMDPs environments.

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2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)

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