Robotic manipulation challenges, such as gras** and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …

Learning control policies offline from pre-recorded datasets is a promising avenue for
solving challenging real-world problems. However, available datasets are typically of mixed …

This paper provides a comprehensive review of the integration of advanced power
management systems and learning techniques in the field of robotics. It identifies the critical …

This letter presents our solution for the Real Robot Challenge III 1, aiming to address
dexterous robotic manipulation tasks through learning from offline data. In this competition …

Transfer reinforcement learning has gained significant traction in recent years as a critical
research area, focusing on bolstering agents' decision-making prowess by harnessing …

This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a
challenge in which a three-fingered robot must carry a cube along specified goal …

Offline policy learning aims to discover decision-making policies from previously-collected
datasets without additional online interactions with the environment. As the training dataset …

Improving the efficiency of deep reinforcement learning for complex systems is a challenging
task. In this work, a model-based deep reinforcement learning method named as RBF-ARX …

Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city,
which combines the wisdom of dynamic people with mobile devices to provide distributed …