Robotic equipment has been playing a central role since the proposal of smart
manufacturing. Since the beginning of the first integration of industrial robots into production …

Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Abstract Machines powered by artificial intelligence increasingly mediate our social, cultural,
economic and political interactions. Understanding the behaviour of artificial intelligence …

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil
application domains, including real-time monitoring, providing wireless coverage, remote …

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 following in …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …