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Structured allocentric representations can boost spatial reasoning in foundation models by up to 18%, even in the absence of visual inputs.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
Strategic recovery from failures is key to deploying robots for complex assembly tasks in the real world.
Humanoid robots can now reliably transport objects on a tray in the real world, thanks to a hierarchical RL approach that isolates and cancels gait-induced disturbances.
