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Conditioning robot policies on a spatiotemporal feature map enables faster and more robust long-horizon mobile manipulation, outperforming traditional image-only approaches.
Encoding "what-is-where" at the pixel level in a single token unlocks SOTA performance in vision-based robot policy learning.
