English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The gap between simulation and the real-world restrains many machine learningbreakthroughs in computer vision and reinforcement learning from beingapplicable in the real world. In this work, we tackle this gap for the specificcase of camera-based navigation, formulating it as following a visual cue inthe foreground with arbitrary backgrounds. The visual cue in the foreground canoften be simulated realistically, such as a line, gate or cone. The challengethen lies in coping with the unknown backgrounds and integrating both. As such,the goal is to train a visual agent on data captured in an empty simulatedenvironment except for this foreground cue and test this model directly in avisually diverse real world. In order to bridge this big gap, we show it'scrucial to combine following techniques namely: Randomized augmentation of thefore- and background, regularization with both deep supervision and tripletloss and finally abstraction of the dynamics by using waypoints rather thandirect velocity commands. The various techniques are ablated in ourexperimental results both qualitatively and quantitatively finallydemonstrating a successful transfer from simulation to the real world.

RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues