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Visual tracks can double the success rates of robot tasks by providing a powerful interface between control and visual prediction.
Forget painstakingly curating 3D-consistent datasets: this RL approach uses a 3D foundation model to *verify* consistency, turning 3D scene editing into a tractable reinforcement learning problem.
Mamba's linear complexity lets you ditch computationally intensive graph attention for flexible job shop scheduling, achieving state-of-the-art performance with faster solving speeds.
