Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning

Kuniyuki Takahashi*, Tetsuya Ogata, Jun Nakanishi, Gordon Cheng, Shigeki Sugano

*この研究の対応する著者

研究成果査読

9 被引用数 (Scopus)

抄録

This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a two-phase learning framework of the body dynamics of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a pre-training phase with motor babbling and a fine-tuning phase with additional learning of the target tasks. In the pre-training phase, we consider active and passive exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations.

本文言語English
ページ(範囲)1002-1015
ページ数14
ジャーナルAdvanced Robotics
31
18
DOI
出版ステータスPublished - 2017 9 17

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用

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