Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

The application of robots in mechanical assembly increases the efficiency of industrial
production. With the requirements of flexible manufacturing, it has become a research …

Learning contact-rich manipulation skills is essential. Such skills require the robots to
interact with the environment with feasible manipulation trajectories and suitable compliance …

This paper provides an adaptive impedance control strategy about docking robot, a locking
mechanism scheme based on the Stewart platform develo** for the problem of excessive …

In this paper, the Intelligent Impedance Control based Wavelet Neural Network (IIC-WNN) is
introduced as a noble adaptive variable impedance approach to enhance the efficiency of …

This paper introduces a discrete-continuous action space to learn insertion primitives for
robotic assembly tasks. Primitives are sequences of elementary actions with certain exit …

Learning from demonstration is one of the most promising methods to counteract the
challenging long-term trends in repetitive industrial assembly. It offers not only a …

A reward function is learned from the expert examples by inverse reinforcement learning
(IRL), which is more reliable than an artificial method. The moth–flame optimization …

This study presents a multimodal human-exoskeleton cooperative control method to realize
different control modes smoothly switching each other with satisfactory stable performance …

Assistive robots for healthcare have witnessed a growing demand over the past decades. In
this letter, we investigate the development of an optimization-based control framework with …