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Achieving a mean nozzle-position error below 10μm while eliminating all collision violations represents a breakthrough in collision-aware trajectory optimization for robotic additive manufacturing.
By explicitly disentangling target features with MLLM guidance, MeGU achieves superior unlearning performance without sacrificing model utility, outperforming existing methods that struggle with the inherent entanglement of semantic concepts in model representations.
Forget finicky time steps: i-PhysGaussian uses implicit integration with 3D Gaussian Splatting to simulate physics with 20x greater stability.
