A Robotic Grasping State Perception Framework With Multi-Phase Tactile Information and Ensemble Learning

Gang Yan*, Alexander Schmitz, Satoshi Funabashi, Sophon Somlor, Tito Pradhono Tomo, Shigeki Sugano

*この研究の対応する著者

研究成果: Article査読

14 被引用数 (Scopus)

抄録

Recently, tactile sensing has attracted increasing attention for robotic manipulation. Predicting the grasping stability before lifting objects and detecting the ongoing/onset of slip after lifting objects are two critical and widely studied tasks in robotic tactile manipulation. Previous methods focus on proposing novel neural networks (NN) architectures towards one of the above two tasks and did not consider that the two tasks are employed in two interconnected action-phases, i.e. grasping and lifting. Therefore, we firstly explore the possibility of constructing a multi-phase, multi-output framework to combine the stability prediction before lifting and the slip detection after lifting. Moreover, to improve the prediction/detection accuracy, we also proposed to explicitly ensemble different NN architectures using various methods, including attention mechanisms. Our experiments are done with 6 state-of-art NN architectures on two datasets including more than 3000 robotic grasps over 80 objects in total. Furthermore our proposals are tested in a real-time robot experiment with unseen objects. Our experimental results show that the proposed multi-phase, multi-output model exhibits more reliable and flexible performance than a single phase model. We also show that using the ensemble of different NN architectures is a viable and practical choice to boost the overall performance.

本文言語English
ページ(範囲)6822-6829
ページ数8
ジャーナルIEEE Robotics and Automation Letters
7
3
DOI
出版ステータスPublished - 2022 7月 1

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 生体医工学
  • 人間とコンピュータの相互作用
  • 機械工学
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 人工知能

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