Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

To solve tasks in complex environments, robots need to learn from experience. Deep
reinforcement learning is a common approach to robot learning but requires a large amount …

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 …

Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient
robot learning from visual observations. Yet the current approaches typically train a single …

Data-driven model predictive control has two key advantages over model-free methods: a
potential for improved sample efficiency through model learning, and better performance as …

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

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 …