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TANGO enables humanoid robots to navigate cluttered spaces with unprecedented language-guided precision, achieving state-of-the-art performance without real-world training.
Standard 2D vision-language models can reliably plan precise 3D robot trajectories simply by enforcing multi-view 2D waypoint consistency and applying classic geometric triangulation.
Stepsize schedules alone can't push gradient descent beyond a convergence rate of $\Omega(T^{-1.9319})$, challenging the pursuit of optimal acceleration.
