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A unified policy can achieve near-perfect performance across diverse robotic embodiments without requiring extensive fine-tuning for specific tasks.
Force-based memory enables VLA models to achieve over 80% success in complex manipulation tasks, outperforming traditional memory methods with minimal computational cost.
SegDiff achieves real-time adaptability in robot manipulation by seamlessly combining continuous trajectory prediction with keypose segmentation, outperforming traditional methods.
