This article presents a skill learning-based hierarchical control strategy for human-robot
cooperative manipulation, which constitutes a novel learning-control system. The high-level …

Learning from demonstration holds the promise of enabling robots to learn diverse actions
from expert experience. In contrast to learning from observation-action pairs, humans learn …

Multitask learning has led to great success in many deep learning applications during the
last decade. However, recent experiments have demonstrated that the performance of …

Many daily tasks exhibit a periodic nature, necessitating that robots possess the ability to
execute them either alone or in collaboration with humans. A widely used approach to …

Students are the future of a nation. Personalizing student interests in higher education
courses is one of the biggest challenges in higher education. Various AI and ML approaches …

To make manipulators fulfill end-effector maintaining tasks, such as writing or drawing tasks
in a complex environment, two hybrid multiobjective motion planing schemes, ie, end …

Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme widely used
in motion generation of robotic systems. However, existing DMP-based methods mainly …

Abstract Model-based control approaches are inadequate to solve the marine surface
vehicle (MSV) path-following problem, especially under adverse environments. To …

This paper proposes an end-to-end self-supervised feature representation network named
Attentive Task-Net or AT-Net for video-based task imitation. The proposed AT-Net …

This article presents a skill learning-based hierarchi-cal control strategy for human–robot
cooperative manipulation, which constitutes a novel learning-control system. The highlevel …