activity
20202022
most citedSemantics for Robotic Mapping, Perception and Interaction: A Survey

103 citations · 152 across the 8 of their papers we have counts for

collaborators

12 papers

cs.RO20225 cited

Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics

Krishan Rana, Ming Xu, Brendan Tidd +2

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert d…

cs.CV202219 cited

Retrospectives on the Embodied AI Workshop

Matt Deitke, Dhruv Batra, Yonatan Bisk +36

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…

cs.CV2021

FSNet: A Failure Detection Framework for Semantic Segmentation

Quazi Marufur Rahman, Niko Sünderhauf, Peter Corke +1

Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely. During deployment, even the most mature segmentation mo…

cs.RO202110 cited

Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness

Ming Xu, Tobias Fischer, Niko Sünderhauf +1

Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exter…

cs.RO2021103 cited

Semantics for Robotic Mapping, Perception and Interaction: A Survey

Sourav Garg, Niko Sünderhauf, Feras Dayoub +9

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and rela…

cs.CV20206 cited

SWA Object Detection

Haoyang Zhang, Ying Wang, Feras Dayoub +1

Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train y…