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<p>We present a data-driven deep reinforcement learning (DRL) method for the optimization of a hierarchically structured control policy that includes the central pattern generator. This method, which is as a whole referred to as the hierarchical reinforcement learning with the central pattern generator (HRL-CPG), is then evaluated with the expectation of its applicability in real robot controls. We observed that stable gait motions were gained in a reasonably small number of trials and errors. Thus, it can be deduced that our HRL-CPG can be a candidate DRL method that enables dynamical systems such as real or realistic robots to adapt to a variety of environments within a moderate physical time.</p>

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Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月2日

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  • Toshiki Watanabe,
  • Akihiro Kubo,
  • Kai Tsunoda,
  • Tatsuya Matsuba,
  • Shintaro Akatsuka,
  • Yukihiro Noda,
  • Hiroaki Kioka,
  • Jin Izawa,
  • Shin Ishii,
  • Yutaka Nakamura

摘要

We present a data-driven deep reinforcement learning (DRL) method for the optimization of a hierarchically structured control policy that includes the central pattern generator. This method, which is as a whole referred to as the hierarchical reinforcement learning with the central pattern generator (HRL-CPG), is then evaluated with the expectation of its applicability in real robot controls. We observed that stable gait motions were gained in a reasonably small number of trials and errors. Thus, it can be deduced that our HRL-CPG can be a candidate DRL method that enables dynamical systems such as real or realistic robots to adapt to a variety of environments within a moderate physical time.