Patch Policy: Efficient Embodied Control via Dense Visual Representations
Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning. Modern robot policies either compress each observation into a single global token, or rely on visual backbones trained from scratch, sacrificing both fine-grained spatial detail and the benefits of large-scale visual pre-training. While there exist policies that do operate on dense patch features like large vision-language-action models (VLAs), they tend to be heavy and slow, inheriting the full cost of a billion-parameter vision-language model (VLM) backbone. We close t
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “Patch Policy: Efficient Embodied Control via Dense Visual Re” ≈ “lllyasviel/ControlNet””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “Patch Policy: Efficient Embodied Control via Dense Visual Re” ≈ “lllyasviel/ControlNet-v1-1””
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzySimilar title/name (fuzzy) · 59%builderz-labs/mission-control →
“Fuzzy title match (0.73): “Patch Policy: Efficient Embodied Control via Dense Visual Re” ≈ “builderz-labs/mission-control””
- LinkedLinked via arxiv author · 85%Gaoyue Zhou →
“Patch Policy: Efficient Embodied Control via Dense Visual Representations”
- LinkedLinked via arxiv author · 85%Zichen Jeff Cui →
“Patch Policy: Efficient Embodied Control via Dense Visual Representations”
- LinkedLinked via arxiv author · 85%Ada Langford →
“Patch Policy: Efficient Embodied Control via Dense Visual Representations”
- LinkedLinked via arxiv author · 85%Bowen Tan →
“Patch Policy: Efficient Embodied Control via Dense Visual Representations”