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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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x.comAxis Robotics
Pushing the limits of humanoid robot agility and capability. How do we build general policies for highly dynamic humanoid control? The solution from @yunshenwang1, Shaohang Zhu, and co-authors has parts:- Flow matching policy learned from action experts- RL of a residual policy on top of this to handle all the complex dynamics of the real world, with domain randomization, power safety, etcOne thing I found fascinating: they broke *dozens* of robots during the development of this paper. It makes
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