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Domain Randomization (DR) is a key component of the data augmentation pipeline at Axis Robotics.By applying DR, we are able to scale verified, high-quality human trajectories by 10x to 100x. During training, we systematically introduce variances in environmental parameters. This prevents the model from relying on spurious visual correlations. The objective is to ensure the policy learns rather than overfitting.To demonstrate the necessity and effectiveness of this approach, we evaluated both DR
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