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G0.5 achieves unprecedented performance in robot reasoning and action by merging decision-making and execution into a single autoregressive framework, outperforming existing models across seven challenging benchmarks.
Unsupervised point cloud denoising can achieve state-of-the-art results by learning to create and enforce consistency between "mirror points" reflecting the underlying surface geometry.
By explicitly reasoning in 3D, VolumeDP leaps ahead of 2D-based imitation learning methods, achieving a remarkable 14.8% improvement on the LIBERO benchmark and robust real-world generalization.
