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

With the increasing adoption of robots across industries, it is crucial tofocus on developing advanced algorithms that enable robots to anticipate,comprehend, and plan their actions effectively in collaboration with humans. Weintroduce the Robot Autonomous Motion (RoAM) video dataset, which is collectedwith a custom-made turtlebot3 Burger robot in a variety of indoor environmentsrecording various human motions from the robot's ego-vision. The dataset alsoincludes synchronized records of the LiDAR scan and all control actions takenby the robot as it navigates around static and moving human agents. The uniquedataset provides an opportunity to develop and benchmark new visual predictionframeworks that can predict future image frames based on the action taken bythe recording agent in partially observable scenarios or cases where theimaging sensor is mounted on a moving platform. We have benchmarked the dataseton our novel deep visual prediction framework called ACPNet where theapproximated future image frames are also conditioned on action taken by therobot and demonstrated its potential for incorporating robot dynamics into thevideo prediction paradigm for mobile robotics and autonomous navigationresearch.

Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots