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Open-Source Practice


Thanks to the development of open-source libraries (such as Tensorflow, Pytorch) and publicly available databases, the last decade has seen the rapid development of AI, especially deep learning.

Compared to AI, new researchers may find it hard to get started and set up experiments with robotics, due to the high entry barrier. The learning curves of robotics are much higher than programming, since the users need sufficient time and intensive training to get familiar with the hardware infrastructures and robot control frameworks. Moreover, due to the lack of hardware-independent software/APIs for robot control, researchers may need to devote further development time to system integration. Therefore, an open-source self-sustaining ecosystem is worth developing for robotics researchers.

Moreover, due to the high cost of robotic platforms and the number of students growing significantly in recent years, an efficient facility training system is needed, which can benefit the students for their group projects or individual research projects. Therefore, advanced digital training technologies should be investigated for high-efficient robotics education in training CDT and other PGR and PGT students. To this end, an online lab system will be developed for i) health and safety induction, ii) facility training, iii) practical experiments with virtual machines, and iv) open-source practice.

In summary, the aim of this project is to develop an Open-Source and Immersive Online Lab System, which can benefit research and education in robotics and enhance the research culture of open-source and collaboration.



We will establish a website and make good use of Wiki technology to enable multiple users to share knowledge and collaborate in a user-friendly editing environment. The website will include several subpages, including Facilities, Forums, Project Documents, Resources, Codes, Latest News, People, Health and Safety.

We encourage technicians and researchers to release the codes through the online lab system, while the open-source codes for low-level control of robotic platforms can be reused by students and new researchers. Researchers can co-create learning materials for robotics projects, and work together to develop customized user interfaces collaboratively for their experiments.

The videos for hardware system professional training will be recorded and shared through the online lab system.

Website Demo:(haven't finished)

WSRender: A Workspace Analysis and Visualization Toolbox for
Robotic Manipulator Design and Verification

Dandan Zhang#; Francesco Cursi#; Guang-Zhong Yang

Workspace analysis is essential for robotic manipulators, which helps researchers to study, evaluate and optimize their designs based on specific criteria with due consideration of ergonomics and usability.  Although workspace analysis is a common research topic, current solutions provide design-specific evaluation and there is a lack of generic software tools for different hardware configurations. This paper presents WSRender, a versatile research-oriented framework for workspace analysis and visualization. It is based on the Orocos Kinematics and Dynamics Library (KDL) and the Matlab Robotic Toolbox. The software architecture is presented with four use cases for demonstrating its practical use in single robot, dual-arm manipulator performance evaluation, multi-robot interaction analysis and  master-slave mapping. The source code of WSRender is made publicly available for the benefit of the research community for the design or evaluation of robotic manipulators.

Full paper link:


TIMS: A Tactile Internet-Based Micromanipulation System with Haptic Guidance for Surgical Training


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.


IoHRT: An Open-Source Unified Framework Towards the Internet of Humans and Robotic Things with Cloud Computing for Home-Care Applications

Dandan Zhang; Jin Zheng; Jialin Lin


The accelerating aging population has led to an increasing demand for domestic robotics to ease caregivers' burden. The integration of Internet of Things (IoT), robotics, and human-robot interaction (HRI) technologies is essential for home-care applications. Although the concept of the Internet of Robotic Things (IoRT) has been utilized in various fields, most existing IoRT frameworks lack ergonomic HRI interfaces and are limited to specific tasks.

This paper presents an open-source unified Internet of Humans and Robotic Things (IoHRT) framework with cloud computing, which combines personalized HRI interfaces with intelligent robotics and IoT techniques. This proposed open-source framework demonstrates characteristics of high security, compatibility, and modularity, allowing unlimited user access. Two case studies were conducted to evaluate the proposed framework's functionalities, evaluating its effectiveness in home-care scenarios. Users' feedback was collected via questionnaires, which indicates the IoHRT framework's high potential for home-care applications.

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