Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

Goal-conditioned policies for robotic navigation can be trained on large, unannotated
datasets, providing for good generalization to real-world settings. However, particularly in …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

In this paper, we study the problem of learning vision-based dynamic manipulation skills
using a scalable reinforcement learning approach. We study this problem in the context of …

General-purpose pre-trained models (" foundation models") have enabled practitioners to
produce generalizable solutions for individual machine learning problems with datasets that …

Moving in complex environments is an essential capability of intelligent mobile robots.
Decades of research and engineering have been dedicated to developing sophisticated …

Neural networks predictions are unreliable when the input sample is out of the training
distribution or corrupted by noise. Being able to detect such failures automatically is …

Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …