Seeing is Learning: Boosting Robotic Agility with Smart Image Augmentation
![The system dissects visual observation into task-relevant and irrelevant regions, employing [latex]SAM[/latex] and [latex]XMem++[/latex] to propagate segmentation masks-then strategically augments the former with task-specific transformations while randomly perturbing the latter via [latex]PixMix[/latex], effectively generating diverse training data by exploiting the interplay between focused manipulation and controlled chaos.](https://arxiv.org/html/2603.04845v1/2603.04845v1/x3.png)
A new approach selectively enhances training images to help robots master complex agricultural tasks through vision alone.
![The system dissects visual observation into task-relevant and irrelevant regions, employing [latex]SAM[/latex] and [latex]XMem++[/latex] to propagate segmentation masks-then strategically augments the former with task-specific transformations while randomly perturbing the latter via [latex]PixMix[/latex], effectively generating diverse training data by exploiting the interplay between focused manipulation and controlled chaos.](https://arxiv.org/html/2603.04845v1/2603.04845v1/x3.png)
A new approach selectively enhances training images to help robots master complex agricultural tasks through vision alone.

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