TransMASK: Masked State Representation through Learned Transformation
TransMASK learns a mask matrix M that transforms raw state observations into a latent representation biased toward relevant elements. By learning this mask through imitation learning, robot policies can ignore irrelevant features like lighting or clutter, making them robust to new environments without needing extra labels or loss function changes.