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

Machine learning techniques rely on large and diverse datasets forgeneralization. Computer vision, natural language processing, and otherapplications can often reuse public datasets to train many different models.However, due to differences in physical configurations, it is challenging toleverage public datasets for training robotic control policies on new robotplatforms or for new tasks. In this work, we propose a novel framework, ExAugto augment the experiences of different robot platforms from multiple datasetsin diverse environments. ExAug leverages a simple principle: by extracting 3Dinformation in the form of a point cloud, we can create much more complex andstructured augmentations, utilizing both generating synthetic images andgeometric-aware penalization that would have been suitable in the samesituation for a different robot, with different size, turning radius, andcamera placement. The trained policy is evaluated on two new robot platformswith three different cameras in indoor and outdoor environments with obstacles.

ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation