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Treating raw visual images as action representations revolutionizes embodied world models, leading to unprecedented generalization and control capabilities.
DockAnywhere lets you train mobile manipulation policies that generalize to new viewpoints from a single demonstration, sidestepping the costly data collection typically needed for robust visuomotor control.
AMRs can now navigate reliably indoors without GPS or external infrastructure, thanks to a new method that simultaneously calibrates magnetometers and estimates robot pose.
Forget static prompts: LDEPrompt dynamically expands and freezes prompts based on layer importance, achieving state-of-the-art performance in class-incremental learning.
Quantum-inspired gating unlocks better knowledge transfer in class-incremental learning, outperforming existing methods by dynamically modeling task relationships.
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.
