This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change.
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics.
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance.
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
1 code implementation • 17 Sep 2016 • Jürgen Leitner, Adam W. Tow, Jake E. Dean, Niko Suenderhauf, Joseph W. Durham, Matthew Cooper, Markus Eich, Christopher Lehnert, Ruben Mangels, Christopher Mccool, Peter Kujala, Lachlan Nicholson, Trung Pham, James Sergeant, Liao Wu, Fangyi Zhang, Ben Upcroft, Peter Corke
We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB).