33 citations · 126 across the 14 of their papers we have counts for
6 papers · 1 filter
Attention-Privileged Reinforcement Learning
Sasha Salter, Dushyant Rao, Markus Wulfmeier +2
Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observat…
Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
Dan Barnes, Rob Weston, Ingmar Posner
This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor…
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
Dan Barnes, Matthew Gadd, Paul Murcutt +2
In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data. The target application…
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones +1
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects…
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning
Fabian B. Fuchs, Adam R. Kosiorek, Li Sun +2
The majority of contemporary object-tracking approaches do not model interactions between objects. This contrasts with the fact that objects' paths are not independent: a cyclist m…
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke +2
Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectur…