33 citations · 126 across the 14 of their papers we have counts for
6 papers · 1 filter
There and Back Again: Learning to Simulate Radar Data for Real-World Applications
Rob Weston, Oiwi Parker Jones, Ingmar Posner
Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty…
Reconstruction Bottlenecks in Object-Centric Generative Models
Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision. However, these are largely restricted t…
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones +5
Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic…
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
Sebastien Ehrhardt, Oliver Groth, Aron Monszpart +4
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained…
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
Li Sun, Daniel Adolfsson, Martin Magnusson +3
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based loca…
Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar
Dan Barnes, Ingmar Posner
This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable poin…