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Northwestern University
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MagicSim revolutionizes robot learning by merging diverse world construction and execution into a single, efficient framework that enhances both evaluation and interaction capabilities.
Achieving superior annotation efficiency and task success rates, AnnotateAnything revolutionizes how 3D assets are prepared for robot manipulation.
LUCID achieves stable and high-quality CT reconstructions even under severe undersampling, minimizing artifacts that plague traditional methods.
A new scheduling framework cuts LLM latency by over 10% while enhancing fairness, challenging the status quo of rigid scheduling policies.
Imagine training embodied agents with a dataset so realistic, humans prefer it 78% of the time – InHabit makes that possible.
Hyperspectral anomaly detection gets a serious upgrade: R2VD ditches scalar reconstruction errors for high-dimensional vector interference patterns, achieving state-of-the-art target detection and background suppression.
Humanoid robots can now groove in sync for a full four minutes on stage, thanks to a model-based control framework that anticipates disturbances and proactively adjusts foot placement.
