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A Deep Learning Framework for Soft Robots with Synthetic Data.

Shageenderan Sapai1, Junn Yong Loo1, Ze Yang Ding2

  • 1School of Information Technology, Monash University Malaysia, Bandar Sunway, Malaysia.

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|August 17, 2023
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Summary
This summary is machine-generated.

This study introduces Transformer TimeGAN (TTGAN), a novel deep learning framework using synthetic data to model soft robot dynamics. This approach reduces the need for extensive real-world data collection, achieving high accuracy with combined synthetic and partial real data.

Keywords:
deep learningsoft sensingsynthetic datatime series generative networks

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep neural networks excel in soft robot modeling but require vast datasets.
  • Acquiring, labeling, and annotating data for soft robots is labor-intensive and often impractical.
  • Existing methods face challenges in capturing complex, nonlinear soft robot dynamics due to data limitations.

Purpose of the Study:

  • To develop a data-driven learning framework that utilizes synthetic data to overcome the limitations of exhaustive data collection for soft robot modeling.
  • To introduce a novel generative adversarial network for generating realistic synthetic time-series data of soft robot behaviors.
  • To enable accurate modeling of soft robot dynamics with reduced reliance on real-world data.

Main Methods:

  • Proposed a Transformer TimeGAN (TTGAN), a novel time series generative adversarial network incorporating a self-attention mechanism.
  • Integrated a conditioning network into TTGAN to generate synthetic data tailored to specific soft robot behaviors.
  • Validated the framework on a pneumatic-based soft gripper, comparing models trained on complete real data, partial real data, and combined synthetic/partial real data.

Main Results:

  • The TTGAN successfully generated synthetic time-series data that accurately reflects realistic soft robot dynamics.
  • A data-driven model trained on a combination of synthetic and partially available original data achieved estimation accuracy comparable to models trained on complete original data.
  • The framework demonstrated the feasibility of using synthetic data to significantly reduce the data acquisition burden.

Conclusions:

  • The proposed TTGAN framework effectively generates high-fidelity synthetic data for soft robot modeling.
  • Combining synthetic data with limited real-world data offers a viable and efficient approach to achieving accurate soft robot models.
  • This synthetic data-driven approach alleviates the bottleneck of extensive data collection in soft robot research and development.