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A unified framework and comprehensive evaluation reveals the surprisingly nuanced performance of diffusion-based data augmentation, showing where it shines and where it falls short in low-data image classification.
Achieve substantially higher success rates in long-horizon mobile manipulation by grounding a vision-language model within a skill-state graph, enabling logically consistent planning and closed-loop replanning.
