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

This work provides an architecture that incorporates depth and tactileinformation to create rich and accurate 3D models useful for roboticmanipulation tasks. This is accomplished through the use of a 3D convolutionalneural network (CNN). Offline, the network is provided with both depth andtactile information and trained to predict the object's geometry, thus fillingin regions of occlusion. At runtime, the network is provided a partial view ofan object. Tactile information is acquired to augment the captured depthinformation. The network can then reason about the object's geometry byutilizing both the collected tactile and depth information. We demonstrate thateven small amounts of additional tactile information can be incredibly helpfulin reasoning about object geometry. This is particularly true when informationfrom depth alone fails to produce an accurate geometric prediction. Our methodis benchmarked against and outperforms other visual-tactile approaches togeneral geometric reasoning. We also provide experimental results comparinggrasping success with our method.

Multi-Modal Geometric Learning for Grasping and Manipulation