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 …

The capability to adapt compliance by varying muscle stiffness is crucial for dexterous
manipulation skills in humans. Incorporating compliance in robot motor control is crucial for …

This investigation is aimed to propose a vibration-driven friction-induced robot structure with
time-varying stiffness. Different from the common vibration-driven robots with force …

Despite the significant progress made in making robots more intelligent and autonomous,
today, teleoperation remains a dominant robot control paradigm for the execution of complex …

Abstract Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric
models that can be used to infer nonlinear functions with time-varying noise. In robotics, they …

The operation and accuracy of industrial robotic arms can be negatively affected by
significant fluctuations in friction forces within their joints, potentially resulting in financial and …

The performance of human–robot interaction can be enhanced by imposing force
constraints through virtual fixture (VF) algorithms. In this article, an adaptive impedance …

Motion planning (MP) is essential but challenging for mobile robots. Most of the existing MP
methods, at each instant, compute an action based on the states of the robot and the …

This research presents a novel robot system that combines active and passive components
to enhance compliance and dependability. The system is based on a continuous variable …

Robotic tasks which involve uncertainty–due to variation in goal, environment configuration,
or confidence in task model–may require human input to instruct or adapt the robot. In tasks …