TL;DR: A robot hand that internal representations of digits. A human can make gestures and see what that means to the robot. This is achieved by using Variational AutoEncoders.
conda env create -f environment.ymlThe model can be seperated into two parts:
Variational AutoEncoder
- The VAE will be used in the inital training process.
- The VAE will learn a latent space that contains
20 elementsthat represent the position of the robotic hand. - During testing, the decoder will be used to generate digits for new lataent space members.
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Classifier
- It classifies the latent space into digits.
- This can be used as a cross checker in case the decoder of the VAE fails to generate a valid output.
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| Task | Time required | Assignee | Current Status |
|---|---|---|---|
| Model VAE | 1 day | Manthan | in progress |
| Model Classifier | 1 day | Sowmith | in progress |
| Train | 3 days | ||
| Validate | 1 day | ||
| Hand Recognition | 3 days |
- Justify the layers of the model
- How to train the model?
- Train the VAE first and then the classifer or
- Train both VAE and classifer simultaneously?
Train the VAE first Use the VAE's encoder and the classifer to train the classifer