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Deep Learning Based Smart Gripper

This repository presents a Deep Learning-based smart gripper integrating tactile sensing, LSTM for temporal features, PPO for adaptive control, and electrode grid feedback. It enables robust slip detection, stable grasping, and intelligent manipulation for advanced robotic applications.

Features

  • Deep Learning architecture for adaptive grasping
  • Slip detection using tactile sensor data
  • PPO-based reinforcement learning for force control
  • LSTM networks for temporal modeling
  • Electrode grid tactile feedback for improved stability

Repository Contents

  • models/ : Neural network architectures (PPO, LSTM, etc.)
  • datasets/ : Sample tactile and grasping datasets
  • scripts/ : Training and evaluation scripts
  • results/ : Experimental results, plots, and logs

Getting Started

  1. Clone the repository: 
    git clone https://github.com/your-username/dl-smart-gripper.git
cd dl-smart-gripper

Citations

@article{sharma2025smartgripper, title={Adaptive Grasping and Control Strategy in a Deep Learning Based Smart Gripper}, author={Sharma, Marut Dev}, journal={Under Review}, year={2025} }