This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Substantial advancements to model-based reinforcement learning algorithms have been
impeded by the model-bias induced by the collected data, which generally hurts …

Unlike traditional robotic hands, underactuated compliant hands are challenging to model
due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually …

Contact can be conceptualized as a set of constraints imposed on two bodies that are
interacting with one another in some way. The nature of a contact, whether a point, line, or …

Human beings possess a remarkable skill for fine in-hand manipulation, utilizing both
intrafinger interactions (in-finger) and finger–environment interactions across a wide range …

Accurate dynamic object manipulation in a robotic hand remains a difficult task, especially
when frictional slip is involved. Prior solutions involve extensive data collection to train …

In-hand manipulation (IHM) is an important ability for robotic hands. This ability refers to
changing the position and orientation of a grasped object without drop** it from the hand …

In-hand manipulation to translate and rotate an object is a challenging problem for robotic
hands. As one solution, robotic hand with belts around fingers () has been developed for …

In order to effectively improve the accuracy and efficiency of students' online learning
behaviour monitoring, an online learning behaviour monitoring method based on face …

Soft robots (SR) are a class of continuum robots that enable safe human interaction with task
versatility beyond rigid robots. This has resulted in their rapid adoption in a number of …