=Paper=
{{Paper
|id=Vol-2329/paper-03
|storemode=property
|title=Hands-on Robotics Teaching with ROS
|pdfUrl=https://ceur-ws.org/Vol-2329/paper-03.pdf
|volume=Vol-2329
|authors=Roel Pieters,Reza Ghabcheloo,Minna Lanz
|dblpUrl=https://dblp.org/rec/conf/erf/PietersGL18
}}
==Hands-on Robotics Teaching with ROS==
https://ceur-ws.org/Vol-2329/paper-03.pdf
Hands-on Robotics Teaching with ROS
Roel Pieters, Reza Ghabcheloo and Minna Lanz
Tampere University of Technology, Tampere, Finland,
roel.pieters@tut.fi
Abstract. This paper describes the teaching efforts in several courses in
our newly started robotics major in Tampere University of technology.
While the fundamentals and theory in robotics are covered by tradi-
tional courses, hands-on experiments and student projects use ROS as
software platform. ROS is introduced by an overview lecture with prac-
tical demonstration and aims to point students towards information and
resources instead of providing ready-made solutions. This approach leads
to much trial-and-error and a steep learning curve that, we believe, is
highly valuable. We reflect on an early evaluation of our approach and
conclude that the advantage of ROS as tool to educate robotics is due
to its holistic nature. From low-level concepts (e.g. interfacing, commu-
nication, diagnostics) to high-level functionality (e.g. visualization, ma-
nipulation, SLAM), all are included in ROS and required to educate the
next generation roboticists.
Keywords: robotics, education, Robot Operating System
1 Introduction
Since the advent of robotics, considerable effort has been put in the development
of theory, algorithms and their implementation. Similarly, such effort should
also be represented in the education of robotics at academic institutes [1–4].
The balance between theoretical foundations and practical work is, however,
not trivial to find, and often complicated by the multitude of available software.
Traditionally, robotics education has gone hand-in-hand with programming (e.g.
C/C++) or higher-level computing environments such as Matlab R . In recent
years, however, research has moved slowly to adopt ROS as middleware [5],
and many other libraries and interfaces offer some form of integration towards
it (e.g. ROS in Matlab). Education has caught up to this and many higher
institutes of education now offer ROS in course form, or require ROS to be used
as programming interface for project work1 . Additionally, professional education
on ROS that is not part of a curriculum can be found in summer schools [6],
conferences2 , online courses and in bookform3 . The take-up of industry, however,
1
https://wiki.ros.org/Courses
2
https://roscon.ros.org
3
https://wiki.ros.org/Books
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is slower but initiatives are ongoing to provide the reliability and robustness that
is required in industrial environments (EU H2020 project ROSIN4 [7]).
As educational design is mostly done behind closed doors, with this brief
paper we report the route taken at Tampere University of Technology, in the
recently started robotics major. By providing an overview of our approach and an
early evaluation of outcomes, we hope to open up a discussion on best practices
with respect to robotics education and ROS.
2 Robotics major
The robotics major at Tampere University of Technology started in September
2017, and offers MSc students to specialize in robotics and the technology re-
lated to it. The degree is offered by the Faculty of Engineering Sciences. The
most significant contributions come from the laboraties of Automation and Hy-
draulic Engineering (AUT) and Mechanical Engineering and Industrial Systems
(MEI), although other units of the university, for example Signal Processing,
also contribute. The two year program (120 ECTS) requires students to have
solid mathematical background and good programming skills. Studies include
the fundamentals of robotics, control of robotics, sensing/perception systems
for robotics, robot programming and project work. After the studies, students
are able to develop robot-based systems. The major consists of four mandatory
courses complemented with elective courses that go deeper in a certain topic. In
several of these courses, experimental work includes the use of ROS to connect
to and control robotic systems, as described in Table 1 and 2.
2.1 TUTRobolab
While all traditional lectures are given in lecture rooms, practical work and
demonstrations make use of a laboratory environment specifically designed for
students. This TUTRobolab offers a place for students to work with robotic
equipment and experiment without major restrictions. Robots and related tech-
nology are for example industrial manipulators (Universal robots, Franka, PRob),
mobile robots (turtlebot, in-house developed robot), a multitude of sensors (2D/3D
ToF cameras, lidar, GPS, IMU, etc.) and different processing platforms (PCs,
embedded PCs, Raspberry Pi). While giving preference to course students, the
lab is available 24/7 to all students interested in robotics and aims to create a
casual learning environment.
2.2 Mandatory courses
The core of the major consists of four mandatory courses, as described in Table
1, designed to educate students on the basics and practicals of robotics and
includes project-based work to prepare them for future professional work life.
All courses emphasize practical implementation and require programming to be
part of the work.
4
http://rosin-project.eu/
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Table 1: Mandatory MSc courses as part of the robotics major
Mechatronics and Robot Programming 5 ECTS Lab exercises
Provides an introduction to sensors and robots. Student groups (2-4 persons) complete
exercises ranging from sensor interfacing and read-out, visualization, installation and
set-up for different processing platforms (PC, Raspberry Pi, wireless networking, SSH)
and robot platforms (motors, turtlebot, crane), to advanced algorithms (simulation,
SLAM, navigation). All exercises are demonstrated by each group, questioned to assess
each member’s understanding and finalized by a report.
Robotics Project work 6 ECTS Group project
Provides project-based group work. Student groups (2-4 persons) pick a topic and
develop a robotics project, emphasized on interdisciplinary collaboration. Lectures are
provided by speakers from industry and academia. The project requires students to de-
velop project planning and management skills, and collaborate on multiple disciplines.
Typical projects include LEGO house building with a UR5 robot, indoor delivery robot
and upgrading an industrial robot manipulator.
Robot Manipulators: 4 ECTS Theory, practicals
Modeling, Control and Programming
Provides a traditional introduction to robot manipulation. In this course students ob-
tain knowledge on robot modeling (kinematics and dynamics, Denavit-Hartenberg no-
tations), and programming for industrial manipulators. Exercises use Matlab and the
course is finalized with an exam.
Fundamentals of Mobile Robots 5 ECTS Theory, practicals
Provides the fundamentals of mobile robotics including localization, mapping and plan-
ning. Extensive exercises use Matlab and ROS.
2.3 Elective courses
Besides the four mandatory courses, additional credits have to be taken to com-
plete the major. These courses can be on specific topics such as computer vision
and machine learning, or more focused on specific fields within robotics, such
as planning and advanced control (e.g. see Table 2). In both mandatory and
elective courses, where possible, ROS is used to ease the education of robotics
and robotics related technology.
2.4 Teaching philosophy
The approach we take to educate robotics on a MSc level, is to find an appro-
priate balance between theory and practice. While the fundamentals can only
be taught by introducing the relevant mathematical theory, a deeper under-
standing is obtained when such theory can be tried and tested in simulation
or experiments. Take for example coordinate systems and their transformation.
3
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Table 2: Elective MSc course as part of the robotics major that includes ROS
Advanced Robotics 5 ECTS Theory, practicals
Provides advanced theory on motion control for robots, such as dynamical systems,
stability theory, visual servoing, force control, etc. Theory is supported by practical
implementation and integration on an advanced robot manipulator (Panda, Franka
Emika GmbH).
Understanding the theory and its use can be assisted by tools such as Matlab5
and ROS6 that ease implementation and visualization.
A choice was made to not have a separate course purely on ROS, as other
institutes offer (such as e.g. ETH Zürich7 ), but only provide an introductory
lecture. This 1.5 hour lecture provides the basics of Linux and ROS, and shows
students where to find information and how to develop their skills. In case of
difficulties or problems student assistants are available to assist and help out
with practical matters.
3 Evaluation
The first intake of the major attracted a significant number of students, where
a selection had to be made based on background and motivation. Such decisions
are difficult as grades and background do not always represent the best selec-
tion criteria. For example, giving preference to students that have experience
and background with ROS might be suitable for elective courses (e.g. Advanced
Robotics), but not for mandatory courses, as ROS will be introduced there. De-
spite this, the majority of students successfully passed, or are about to pass,
the courses. Evaluation of (ROS) skills was assessed by oral examination and
practical demonstration of the developed software.
3.1 Lessons learned
Lessons learned for the educators of the robotics major are mostly related to the
time and effort necessary to prepare course work and practical experiments. It
is very easy to underestimate the time required to set up simulations and ex-
periments and verify that all will function properly on different platforms. This
applies similarly for laboratory systems that are in continuous use for different
projects. Software versions and hardware differences might simply not be com-
patible. It is difficult to anticipate for this and to estimate how much time and
effort it takes for students to master such realization.
5
https://se.mathworks.com/help/robotics/gs/coordinate-systems-in-robotics.html
6
https://wiki.ros.org/tf2
7
http://www.rsl.ethz.ch/education-students/lectures/ros.html
4
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Even though ROS is a major step forward in providing a standard in robotics
education, this also is a drawback. It is very convenient to install a package,
execute the algorithms it provides and demonstrate a functioning system. This
does not imply, however, that the fundamentals behind the algorithms and robot
are understood. Such understanding has to come from studying the algorithms
and experimenting with different parameters, or by developing and/or extending
the methods. Again, we believe that by providing the tools and problems, and
requiring from students a solution, will lead to a deeper understanding of the
theory.
Additionally, not all students have the same background, leading to gaps
in programming capabilities and hands-on experiments with software coding.
This leads to big differences in outcomes and progress throughout a course. One
solution to this is to require a certain level of programming skills in order to be
accepted in the major. Despite this, we found good results in creating group work
with different skill sets. Even though this might cause one person to take the
lead in project work, this can be compensated to ensure all participants have
to demonstrate the learned skills. Group work also leads to students learning
valuable skills necessary in professional work life, such as educating colleagues,
team work, effctive time and project management, and communication.
Feedback that was obtained from students after finalizing a course gave valu-
able information on how to improve the education. Issues that were raised were
for example, the lack of a lab technician for quick questions and small practical
issues, and the deployment of a virtual workspace where students can collabo-
rate, discuss and share ideas (e.g., Slack8 ).
3.2 Post-graduate requirements
In Finland, it is common to do a MSc thesis in a company, where ROS is most
likely not being used. Why should an academic institute then spend so much
effort on educating their students with ROS? Our answer to this is that it’s
impossible to ensure a common framework that is supported by all involved
partners. Even the most common tool for studying engineering (Matlab), is still
underrepresented in industry due to its high cost. Our main aim is to educate
future engineers with the skills and capabilities to design robotic systems. This
whole overview of robotics ranges from interfacing sensors and actuators, to
control architectures and high-level industrially relevant applications. With so
much diversity a common ground is found in ROS, as it supports all. Moreover,
different programming languages can be used, to accomodate different educa-
tional backgrounds. Through ROS, students practice C/C++ or Python which
is commonly used in industry, and learn what a complex robot control system
should include.
8
https://slack.com/
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4 Conclusion
This short paper described the efforts made in Tampere University of Technol-
ogy with respect to robotics education. Our approach is to balance theory with
significant hands-on experimental work, by utilizing the Robot Operating Sys-
tem. The advantage that ROS has over other robotics software, such as Matlab,
is that it provides a complete overview and implementation of required tools.
From low-level concepts such as communication and interfacing to high-level
algorithms, it educates roboticists the basics and pushes the state-of-the-art.
Drawbacks of ROS are found in the limited adoption in industry and in compat-
ibility or support issues (e.g., unsupported versions, hardware/software version
mismatch). Despite this, a first evaluation of our robotics major suggests that
students benefit greatly from a community-supported initiative such as ROS.
The large user-base and growing support for robotic equipment, means that in-
formation can be found and asked for on-line. This brings a steep learning curve
that, we believe, is highly valuable.
Acknowledgements
The work presented is financed by the Academy of Finland project: ”Competitive
funding to strengthen university research profiles”, decision number 310325.
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and C. Robotics
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