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Sparsh
Overview :
Sparsh is a series of general tactile representations trained through self-supervised algorithms such as MAE, DINO, and JEPA. It can generate useful representations for DIGIT, Gelsight'17, and Gelsight Mini, significantly outperforming end-to-end models on downstream tasks proposed by TacBench while supporting data-efficient training for new downstream tasks. The Sparsh project includes PyTorch implementations, pre-trained models, and datasets released alongside Sparsh.
Target Users :
The target audience for Sparsh includes researchers and developers in the fields of robotics, artificial intelligence, and computer vision. It is particularly suitable for professionals who need to conduct research or develop applications in the realm of tactile sensing. The self-supervised and multi-task learning framework provided by Sparsh aids in enhancing model performance and data efficiency, while its open-source nature allows for customization and further development.
Use Cases
- In robotic grasping tasks, use Sparsh to predict grasping forces to optimize grasping strategies.
- In medical assistive devices, leverage Sparsh for tactile feedback to enhance device interactivity and safety.
- In industrial inspection, apply Sparsh for product quality assessment, improving inspection accuracy through tactile data analysis.
Features
- Self-supervised learning algorithms: Sparsh is trained using self-supervised learning algorithms like MAE, DINO, and JEPA.
- Multi-tactile sensor support: Capable of generating useful representations for various tactile sensors, including DIGIT, Gelsight'17, and Gelsight Mini.
- Superior downstream task performance: Sparsh greatly surpasses end-to-end models in downstream tasks proposed by TacBench.
- Data-efficient training: Sparsh supports data-efficient training for new downstream tasks.
- Pre-trained models and datasets: Provides PyTorch implementations, pre-trained models, and datasets for ease of use by researchers and developers.
- Multiple downstream task support: Sparsh supports various downstream tasks, including force estimation, slip detection, and pose estimation.
- Open-source code and models: The code and models for Sparsh are open-sourced on GitHub, allowing for community contributions and improvements.
How to Use
1. Clone the Sparsh repository locally: Use the git clone command to clone the Sparsh GitHub repository.
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2. Create an environment: Set up a conda environment based on the provided environment.yml file and activate it.
3. Download the dataset: Follow the guidelines to download and set up the pre-trained dataset.
4. Train the model: Begin training the Sparsh model using the train.py script and configuration file.
5. Fine-tune the model: Use the train_task.py script to fine-tune the Sparsh model for specific downstream tasks.
6. Test the model: Utilize the test_task.py script to evaluate the performance of the trained model.
7. Visualization demo: Run the demo_forcefield.py script for force field visualization.
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