  Microsurgery involves the dexterous manipulation of delicate tissue or
fragile structures such as small blood vessels, nerves, etc., under a
microscope. To address the limitation of imprecise manipulation of human hands,
robotic systems have been developed to assist surgeons in performing complex
microsurgical tasks with greater precision and safety. However, the steep
learning curve for robot-assisted microsurgery (RAMS) and the shortage of
well-trained surgeons pose significant challenges to the widespread adoption of
RAMS. Therefore, the development of a versatile training system for RAMS is
necessary, which can bring tangible benefits to both surgeons and patients.
  In this paper, we present a Tactile Internet-Based Micromanipulation System
(TIMS) based on a ROS-Django web-based architecture for microsurgical training.
This system can provide tactile feedback to operators via a wearable tactile
display (WTD), while real-time data is transmitted through the internet via a
ROS-Django framework. In addition, TIMS integrates haptic guidance to `guide'
the trainees to follow a desired trajectory provided by expert surgeons.
Learning from demonstration based on Gaussian Process Regression (GPR) was used
to generate the desired trajectory. User studies were also conducted to verify
the effectiveness of our proposed TIMS, comparing users' performance with and
without tactile feedback and/or haptic guidance.
