Mid-Level Visual Representations is a brand that focuses on improving the generalization and sample efficiency of visuomotor policies. With contributions from UC Berkeley, FAIR, and Stanford, they conducted a large-scale study on how to integrate pretrained perception networks into active tasks. By incorporating a mid-level perception skill set, which includes features like distance estimators and edge detectors, their approach enhances the policy's understanding of the world compared to raw images.

This brand found that leveraging mid-level perception offers significant advantages over training from scratch, particularly in navigation-oriented tasks. It allows agents to generalize to situations where traditional methods fail and makes training more sample efficient. To achieve these gains, careful selection of mid-level perceptual skills is crucial.

Mid-Level Visual Representations also introduces the concept of the max-coverage feature set, which provides better generic perception. This brand's research and methodologies have been published in prestigious conferences and journals, and they offer various resources like a policy explorer, performance curves, and code for further exploration