Autonomous underwater vehicle plays a more and more important role in the exploration of
marine resources. Path planning and obstacle avoidance is the core technology to realize …

Designing a controller for autonomous vehicles capable of providing adequate performance
in all driving scenarios is challenging due to the highly complex environment and inability to …

We demonstrate that models trained only in simulation can be used to solve a manipulation
problem of unprecedented complexity on a real robot. This is made possible by two key …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that
can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

Designing agile locomotion for quadruped robots often requires extensive expertise and
tedious manual tuning. In this paper, we present a system to automate this process by …

We consider the problem of transferring policies to the real world by training on a distribution
of simulated scenarios. Rather than manually tuning the randomization of simulations, we …

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-
scale testbed for unsupervised domain adaptation across visual domains. Unsupervised …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …