gaoyuezhou/patch_policy

This repository accompanies a research paper titled "Patch Policy: Efficient Embodied Control via Dense Visual Representations." The work comes from researchers at New York University, Meta AI, and AMI Labs. The project has an accompanying website and a paper hosted on arXiv, but the repository itself does not yet contain usable code. The research focuses on embodied control, which is the challenge of getting robots or other physical systems to act in the world based on what they see. The phrase "dense visual representations" in the title suggests the method relies on fine-grained visual information, breaking down images into small patches rather than working with a single summary view. The goal appears to be making this process efficient, so a system can process visual input and decide on actions without excessive computational cost. The README includes a method overview image and an embedded video, both of which likely illustrate how the approach works in practice. However, the text of the README is minimal. It does not explain the method in detail, describe any installation or usage steps, or document any application programming interface. The authors state that code will be released soon and ask readers to stay tuned. Beyond the paper citation block, there is nothing else to read here. There are no topics listed, no description provided, and no programming language specified. A non-technical reader who arrives at this repository will find that it currently serves as a placeholder pointing to the paper and the project website. Anyone who wants to understand what Patch Policy actually does would need to consult the arXiv paper or the project page linked in the README. The repository may become more useful once the authors publish the promised code.