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Huazhong University of Science and Technology
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SRDP achieves unprecedented stability and quality in robotic polishing by seamlessly integrating stage-awareness with roughness constraints, outperforming existing methods.
Today's LLM agents are surprisingly passive, missing opportunities to proactively learn and remember user preferences that could dramatically improve their long-term usefulness.
Hand-eye calibration gets a 67% accuracy boost in high-uncertainty scenarios thanks to a new optimization framework that cleverly avoids explicit uncertainty modeling.
