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 …

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 …

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

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

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 …

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 …

Understanding Theory of Mind should begin with an analysis of the problems it solves. The
traditional answer is that Theory of Mind is used for predicting others' thoughts and actions …

Image animation consists of generating a video sequence so that an object in a source
image is animated according to the motion of a driving video. Our framework addresses this …

Recent unsupervised pre-training methods have shown to be effective on language and
vision domains by learning useful representations for multiple downstream tasks. In this …