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High-Resolution Contact Localization and Three-Axis Force Estimation with a Sparse Strain-Node Tactile Interface

Yanyan Wu1, Hanhan Wu1, Yifei Han1

  • 1School of Advanced Manufacturing, Sun Yat-sen University, Shenzhen 518005, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel sparse strain-node tactile interface device (SSTID) for precise human-robot interaction. It enables accurate 3D force and location sensing using minimal channels, reducing cost and complexity.

Keywords:
contact localizationfew-shot fine-tuningsim-to-real transfersparse tactile sensingthree-axis force estimation

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Area of Science:

  • Robotics
  • Materials Science
  • Sensor Technology

Background:

  • High-resolution tactile sensing is vital for human-robot interaction and manipulation.
  • Current methods face limitations in sensing area, channel density, and wiring costs.
  • Sparse strain readout presents challenges in accurately estimating location and multi-axis forces due to signal coupling and nonlinearity.

Purpose of the Study:

  • To develop a cost-effective and scalable sparse tactile interface for precise contact localization and three-axis force estimation.
  • To overcome the limitations of dense sensor arrays and extensive calibration procedures.
  • To enable robust joint estimation of contact location (x,y) and forces (Fx,Fy,Fz) using a minimal number of strain channels.

Main Methods:

  • A sparse strain-node tactile interface device (SSTID) with an optimized three-module layout using particle swarm optimization.
  • A strain-node contact-state decoding framework (SCDF) utilizing a lightweight multilayer perceptron.
  • A two-stage sim-to-real training strategy involving Finite Element Method (FEM) pretraining and few-shot real-data adaptation.

Main Results:

  • The SSTID successfully enabled contact localization (x,y) and three-axis force estimation (Fx,Fy,Fz) with only nine strain channels.
  • The SCDF demonstrated accurate contact-state decoding across the full workspace.
  • The proposed sim-to-real strategy facilitated effective model training and adaptation.

Conclusions:

  • The developed SSTID offers a low-cost and scalable solution for high-resolution tactile sensing.
  • The optimized design and decoding framework effectively address challenges in sparse strain readout.
  • This approach supports broader implementation of advanced tactile sensing in robotics and manipulation.