SCIENCE@home

Learning-based Six-axis Force/Torque Estimation Using GelStereo Fingertip Visuotactile Sensing

Chaofan Zhang,Shaowei Cui,Yinghao Cai,Jingyi Hu,Rui Wang,Shuo Wang

Published 2022 in IEEE/RJS International Conference on Intelligent RObots and Systems

ABSTRACT

Visuotactile sensors have recently attracted much attention in robot communities due to the benefit of high spatial resolution sensing. However, force/torque estimation by visuotactile sensors remains a challenging problem. In this paper, we propose a learning-based six-axis force/torque estimation network using GelStereo visuotactile sensor, which can provide two-dimensional (2D) and three-dimensional (3D) displacements of markers embedded in the sensor surface. The convolutional neural networks are employed to extract multi-modal tactile deformation features; and a novel contact positional encoding method is proposed to eliminate the influence of translation invariance in convolutional operators. The well-trained model achieves the best RMSE of 0.290 N in force and 0.0084 Nm in torque. Furthermore, the proposed force/torque estimation network is integrated with a force-feedback policy for adaptive grasping tasks. The experimental results demonstrate the effectiveness of the proposed method and its potential application in robotic grasping and manipulation tasks.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    IEEE/RJS International Conference on Intelligent RObots and Systems

  • Publication date

    2022-10-23

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/IROS47612.2022.9981100
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-25 of 25 references · Page 1 of 1

CITED BY

Showing 1-17 of 17 citing papers · Page 1 of 1