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Real-time, uncertainty-aware signed distance functions are now possible, thanks to a new kernel regression and Gaussian process hybrid that outperforms existing methods in accuracy and speed.
By encoding physical priors into a graph transformer, PhysGraph achieves superior bimanual manipulation with half the parameters and zero-shot generalization to new tools, suggesting learned physics can be replaced with structured inductive biases.
