AC-LAM enforces additive composition on latent actions from visual transitions, yielding more structured and calibrated motion latents that improve downstream embodied policy learning over prior LAMs.
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Linear mappings in feature space can reconstruct a wide range of image manipulations including semantic edits, suggesting that feature representations are approximately linearly organized.
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Learning Additively Compositional Latent Actions for Embodied AI
AC-LAM enforces additive composition on latent actions from visual transitions, yielding more structured and calibrated motion latents that improve downstream embodied policy learning over prior LAMs.
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FeatMap: Understanding image manipulation in the feature space and its implications for feature space geometry
Linear mappings in feature space can reconstruct a wide range of image manipulations including semantic edits, suggesting that feature representations are approximately linearly organized.