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A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
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aeoniv/Robotics-Object-Pose-Estimation
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A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
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- Python 58.3%
- C# 31.4%
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- Dockerfile 2.2%
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