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.
- 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
models/: Neural network architectures (PPO, LSTM, etc.)datasets/: Sample tactile and grasping datasetsscripts/: Training and evaluation scriptsresults/: Experimental results, plots, and logs
- Clone the repository:
git clone https://github.com/your-username/dl-smart-gripper.git cd dl-smart-gripper
@article{sharma2025smartgripper, title={Adaptive Grasping and Control Strategy in a Deep Learning Based Smart Gripper}, author={Sharma, Marut Dev}, journal={Under Review}, year={2025} }