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Tabular foundation models can dramatically accelerate robot policy learning by enabling efficient global exploration within dynamically constructed policy subspaces.
Kernel Sum-of-Squares optimization can guide sampling-based trajectory optimization out of local minima in high-dimensional contact-rich manipulation tasks.
