98 citations · 390 across the 26 of their papers we have counts for
9 papers · 1 filter
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Jake Bruce, Niko Suenderhauf, Piotr Mirowski +2
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issu…
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney +6
A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot…
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dimity Miller, Lachlan Nicholson, Feras Dayoub +1
Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and r…
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
Trung Pham, Thanh-Toan Do, Niko Sünderhauf +1
This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over sce…
Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM
Niko Sünderhauf, Michael Milford
Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semanti…
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Sean McMahon, Niko Sünderhauf, Ben Upcroft +1
Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspe…
Episode-Based Active Learning with Bayesian Neural Networks
Feras Dayoub, Niko Sünderhauf, Peter Corke
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, s…
What Would You Do? Acting by Learning to Predict
Adam Tow, Niko Sünderhauf, Sareh Shirazi +2
We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perfo…
Deep Learning Features at Scale for Visual Place Recognition
Zetao Chen, Adam Jacobson, Niko Sunderhauf +5
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using gen…