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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.
