Evidence map›Paper›PMID 40175434›Full record

ArticleScientific reports2025

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Toshiki Watanabe *Kyoto University, Kyoto, Japan.
Akihiro Kubo *Advanced Telecommunications Research Institute, Kyoto, Japan.
Kai Tsunoda *Kyoto University, Kyoto, Japan.
Tatsuya MatsubaAISIN Corporation, Kariya, Japan.
Shintaro AkatsukaAISIN Corporation, Kariya, Japan.
Yukihiro NodaAISIN Corporation, Kariya, Japan.
Hiroaki KiokaAISIN Corporation, Kariya, Japan.
Jin IzawaAISIN Corporation, Kariya, Japan.
Shin IshiiKyoto University, Kyoto, Japan. ishii@i.kyoto-u.ac.jp.
Yutaka NakamuraGuardian Robotics Project, RIKEN, Wako, Japan.

Funding

Japan Society for the Promotion of Science 22H04998
6 · The paper itself

Abstract

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.

Identifiers

PMID40175434
PMCPMC11965509

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.