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/

Zendy Plus

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

Zendy Tools

Zendy Open

Placing objects is a fundamental task for domestic service robots (DSRs).Thus, inferring the collision-risk before a placing motion is crucial forachieving the requested task. This problem is particularly challenging becauseit is necessary to predict what happens if an object is placed in a cluttereddesignated area. We show that a rule-based approach that uses plane detection,to detect free areas, performs poorly. To address this, we develop PonNet,which has multimodal attention branches and a self-attention mechanism topredict damaging collisions, based on RGBD images. Our method can visualize therisk of damaging collisions, which is convenient because it enables the user tounderstand the risk. For this purpose, we build and publish an original datasetthat contains 12,000 photo-realistic images of specific placing areas, withdaily life objects, in home environments. The experimental results show thatour approach improves accuracy compared with the baseline methods.

Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations