Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Motion control is crucial for multilegged robot locomotion and task completion. This study
aims to address the fundamental challenges of inadequate foot tracking and weak leg …

We introduce Genie, the first* generative interactive environment* trained in an
unsupervised manner from unlabelled Internet videos. The model can be prompted to …

In this study, we are interested in imbuing robots with the capability of physically-grounded
task planning. Recent advancements have shown that large language models (LLMs) …

In autonomous driving predicting future events in advance and evaluating the foreseeable
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

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

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 …

The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …