Chao Zeng

TL;DR: Physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface.

TL;DR: This paper develops a framework that enables the robot to learn both movement and stiffness features from the human tutor and can be efficiently realized by the proposed framework.

TL;DR: A new framework to facilitate robot skill generalization is proposed, in that the learned skills are first segmented into a sequence of subskills automatically, then each individual subskill is encoded and regulated accordingly.

TL;DR: A physical human–robot interaction system which allows robots to learn variable impedance skills from human demonstrations and enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller is developed.

TL;DR: A novel representation model named human- like compliant movement primitives (Hl-CMPs) which could allow a robot to learn human-like compliant behaviors to enable robotic compliant manipulation.