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GS-Agent transforms natural language into intricate 4D worlds, showcasing a new era of automated creative content generation that rivals traditional manual methods.
Hierarchical sub-goal policies enable robots to adapt to novel tasks with just a few human demonstrations, significantly enhancing their manipulation capabilities.
Today's robot policies and VLAs fall apart when faced with unexpected challenges requiring reasoning, strategy adaptation, and robustness, even after fine-tuning on similar tasks.
Stabilizing test-time training with an elastic prior lets you reconstruct 4D scenes from long video sequences without catastrophic forgetting, even with smaller memory chunks.
