14 citations · 19 across the 4 of their papers we have counts for
8 papers
CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
Jonathan Sadeghi, Jenny Seidenschwarz, Jesse Allardice +3
Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distrib…
PROWL: Prioritized Regret-Driven Optimization for World Model Learning
Ahmet H. Güzel, Jenny Seidenschwarz, Benjamin Graham +3
Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream p…
Reimagining an autonomous vehicle
Jeffrey Hawke, Haibo E, Vijay Badrinarayanan +1
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development. Solving the technology…
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
Anthony Hu, Zak Murez, Nikhil Mohan +5
Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's…
Urban Driving with Conditional Imitation Learning
Jeffrey Hawke, Richard Shen, Corina Gurau +8
Hand-crafting generalised decision-making rules for real-world urban autonomous driving is hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations…
Learning to Drive from Simulation without Real World Labels
Alex Bewley, Jessica Rigley, Yuxuan Liu +4
Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems. Training and evaluating methods purely in simulatio…
Learning to Drive in a Day
Alex Kendall, Jeffrey Hawke, David Janz +6
We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane follow…
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
Jeffrey Hawke, Alex Bewley, Ingmar Posner
This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the place…