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Robots can now perform continuous mobile manipulation tasks with significantly improved reliability and efficiency by seamlessly switching between trajectory tracking and error compensation.
Achieve state-of-the-art performance in multimodal remote sensing semantic segmentation with significantly fewer trainable parameters by using a novel parameter-efficient and modality-balanced symmetric fusion framework.
Injecting physics-based priors derived from MLLMs at decoding time significantly boosts weather forecasting accuracy and stability, even in long autoregressive rollouts.
Achieve 2.5x higher success in UAV navigation by decoupling target generation from progress monitoring, enabling safer and more efficient zero-shot flight.
