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Replication of the model in Time-Contrastive Networks: Self-Supervised Learning from Video for a manipulation task in a simulation.

License: MIT License

Python 73.74% TeX 26.26%

charlatan's Introduction

charlatan

This project attempts to replicate results obtained in the paper Time-Contrastive networks: self-supervised learning from video. We perform experiments to show that videos taken from a single viewpoint can be used to learn a distance preserving embedding of a scene onto a vector space. We attempt to teach a simulated robotic arm to imitate itself performing different trajectories using reinforcement learning and the learned embedding. The results show that an embedding can be learned in a self-supervised manner. However, learning to imitate proved itself more challenging.

More in depth discussion about our project can be found from our report!

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kekeblom avatar gamleksi avatar

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