Prototyping Educational and Scientiﬁc Devices with a Custom Python

Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap Motion

Integration Case Study

Jakub Malinowski

, Piotr Artiemjew

2 a

and Wojciech Cybowski

Scientiﬁc Circle of Robotics, Univeristy of Warmia and Mazury in Olsztyn, Poland

University of Warmia and Mazury in Olsztyn, Poland

Keywords:

Educational Technology, Prototyping Tools, Python Programming, Lego Mindstorms, Leap Motion

Controller, Interactive Learning Environments.

Abstract:

This paper introduces a custom Python library speciﬁcally developed to enable the rapid prototyping of de-

vices for educational and scientiﬁc purposes using the Lego Mindstorms Robot Inventor set. The integration

of Leap Motion as a case study exempliﬁes the library’s extensive capabilities in facilitating intuitive and inter-

active control mechanisms for mobile robots. Through the Leap Motion controller, this research explores the

innovative application of real-time hand gesture recognition, allowing users to command the robot with natural

gestures for movement, direction, and speed adjustments. The decision to use Leap Motion as a case study

serves to highlight the library’s adaptability and effectiveness in processing and interpreting gesture data,

thereby offering a glimpse into the broader potential of the library for various applications. This approach

demonstrates how educators and researchers can utilize the library to create engaging learning experiences

and conduct exploratory projects in robotics and beyond. By detailing the process of integrating Leap Motion

with the Robot Inventor set through our Python library, the paper underscores the potential of such tools in en-

hancing interactive learning environments and advancing the ﬁeld of educational technology. The open-source

nature of the library, coupled with its modular design, ensures that it can be easily extended and adapted to ﬁt

a wide range of educational and scientiﬁc prototyping needs. This paper thus presents a valuable resource for

those looking to explore the frontiers of interactive technology in education and research.

1 INTRODUCTION

The use of prototyping in education and research is

a very valuable practice. In general, it provides an

opportunity to teach in an interesting way, by observ-

ing the effects in the real world. And to create low-

cost initial versions of devices to verify research hy-

potheses. The current work is located in the area of

application of prototyping in learning to develop AI

techniques and creating hybrid IoT and robotic sys-

tems for research scenarios. In our laboratory (UWM,

2024), we have many years of experience in the use

of hardware in teaching intelligent robotics and ar-

tiﬁcial intelligence. Among others, we specialise in

the development of gesture control systems, having

experience in controlling Roomba, Nao, Spot, lego

robots - see for example the work (

Zmudzi

nski et al.,

2016). We develop systems to train robotics and AI

https://orcid.org/0000-0001-5508-9856

algorithms using augmented reality markers - see for

example (Szpakowska et al., 2023). We generally de-

velop technology for algorithm learning and research.

In this paper, we focus on connecting the Robot

Inventor kit with the Leap Motion controller. We use

a Python library we developed in our robotics lab (Cy-

bowski, 2023) to help the robot work with a dedicated

Leap Motion control software suite. This work not

only highlights how gesture control can be applied in

robotics but also shows our lab’s dedication to push-

ing forward the development of educational and sci-

entiﬁc devices with easy-to-use and accessible tech-

nology

The next sections of this paper have the follow-

ing content. In sections 1.1 and 1.2 we have a dis-

cussion of the potential applications of our system in

education and research. In section 2, we discuss the

individual components of the project, the implemen-

tation part and demonstration of the project. Finally,

we summarise the work and discuss future works.

542

Malinowski, J., Artiemjew, P. and Cybowski, W.

Prototyping Educational and Scientiﬁc Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap Motion Integration Case Study.

DOI: 10.5220/0012712600003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 542-549

ISBN: 978-989-758-697-2; ISSN: 2184-5026

Figure 1: Graphical presentation of the system - use of Leap motion as a case study.

Let’s start by discussing the potential applications

of our lego mindstorms robot inventor 5in1 control

python library in educational contexts.

1.1 Educational Applications

Robotics Education. The developed Python library

and Leap Motion integration offer signiﬁcant ad-

vantages in robotics education. By providing stu-

dents with a hands-on experience in controlling a

robot through intuitive hand gestures, this technology

enhances the learning process (Anderson and Dill,

2000). It promotes a deeper understanding of robotics

concepts, and sensor integration. Students can exper-

iment with different gestures and observe their imme-

diate impact on robot behavior, cultivating a practical

understanding of robotics principles.

Engaging STEM Learning. The use of gesture-

based control makes learning about science, technol-

ogy, engineering, and mathematics (STEM) more en-

gaging and interactive. It attracts the interest of stu-

dents at various educational levels, from elementary

schools to universities. The tool can be integrated

into STEM curriculum modules, enabling educators

to teach abstract concepts in a real and exciting man-

ner (Folk, 1981).

Prototyping and Design Projects. Students can

use the Python library to design and build their

robotic prototypes, experimenting with different con-

trol strategies. This hands-on approach encourages

creativity and problem-solving skills (Resnick et al.,

2009). Additionally, it allows students to gain prac-

tical experience in the iterative design process, from

concept development to prototype testing and reﬁne-

ment.

Interdisciplinary Learning. The ﬂexibility of the

Python library and its compatibility with other de-

vices and sensors create opportunities for interdisci-

plinary learning. Students from diverse backgrounds,

including computer science, engineering, and design,

can collaborate on projects that incorporate both hard-

ware and software elements (Cagiltay et al., 2014).

This promotes a comprehensive understanding of

technology integration.

Accessible Learning. The open-source nature of

the library ensures accessibility, making it an inclu-

sive tool for diverse learners. It can be easily shared

among educational institutions, ensuring that students

from various backgrounds have equal opportunities

to explore robotics and human-computer interaction

(Miglino et al., 1999).

Research Collaboration. Educators and re-

searchers can collaborate on innovative teaching

methods and curricula development using this tech-

nology. By sharing experiences and best practices,

they can continually improve robotics education and

contribute to the growth of the ﬁeld.

In summary, the developed Python library and

Leap Motion controller integration offer valuable re-

sources for enhancing robotics education. They pro-

vide a platform for engaging, interactive, and interdis-

ciplinary learning experiences, empowering students

to explore the exciting world of robotics while en-

couraging creativity, problem-solving skills, and col-

laboration. Moreover, the open-source nature of the

tool promotes accessibility and collaboration among

educators and researchers, further advancing the ﬁeld

of robotics education.

Let’s move on to discuss an overview of the re-

search and teaching applications of our project re-

sults.

Prototyping Educational and Scientiﬁc Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap

Motion Integration Case Study

543

1.2 Research Applications

Rapid Prototyping and Experimentation. The

Python library and Leap Motion controller integration

provide researchers with a resourceful platform for

rapid prototyping and experimentation in the ﬁeld of

robotics and human-machine interaction. Researchers

can quickly develop and test novel control algorithms,

user interfaces, and interaction paradigms, reducing

development time and costs (Lawitzky et al., 2013).

Human-Robot Interaction Studies. The gesture-

based control system enables researchers to conduct

in-depth studies on human-robot interaction (HRI).

By using hand gestures to control robots, researchers

can investigate how users perceive and interact with

robotic systems. This technology can be applied in

various HRI contexts, such as healthcare, manufac-

turing, and service robotics (Neto et al., 2019).

Usability Testing. Researchers can use the tool to

conduct usability testing of robotic systems and inter-

faces. By analyzing user interactions and feedback,

they can ﬁne-tune the design of robotic applications

to improve user experience and efﬁciency. This is par-

ticularly valuable in the development of robotic solu-

tions for speciﬁc industries and user groups (Chac

et al., 2021).

Sensor Integration and Data Collection. The

Python library’s compatibility with various sensors

and devices allows researchers to integrate additional

hardware components seamlessly. This ﬂexibility en-

ables the collection of rich sensor data, facilitating

research in areas such as environmental monitoring,

object recognition, and spatial awareness (Zhang and

Doyle, 2023).

Collaborative Research. The open-source nature

of the library promotes collaboration among re-

searchers and institutions. Researchers can share their

code and experiences, promoting a community of

experts in robotics and human-machine interaction.

Collaborative efforts can lead to the development of

standardized interfaces and best practices in the ﬁeld

(Matthews and Greenspan, 2020).

Cross-Disciplinary Research. The tool’s adapt-

ability extends its utility to cross-disciplinary research

projects. Researchers from diverse ﬁelds, including

robotics, computer science, psychology, and engi-

neering, can collaborate on projects that use gesture-

based control and robotics technology to address

complex research questions (Rex Hartson, 1998).

Validation and Benchmarking. Researchers can

use the library to validate and benchmark their control

algorithms and robotic systems. By comparing the

performance of their solutions with the gesture-based

control system, they can assess the effectiveness of

their approaches and contribute to the advancement

of the ﬁeld (Araujo et al., 2023).

In conclusion, the Python library and Leap Mo-

tion controller integration offer researchers a pow-

erful tool for advancing their work in robotics and

human-machine interaction. Whether it’s for rapid

prototyping, in-depth HRI studies, usability testing,

or cross-disciplinary research, this technology pro-

vides a ﬂexible and accessible platform. The open-

source nature of the tool encourages collaboration and

knowledge-sharing among researchers, driving inno-

vation in the ﬁeld and contributing to the development

of robust and user-friendly robotic systems.

Now lets discuss in detail how we created our

smart gesture control system, which is a demonstra-

tion of the use of our library.

2 PROJECT COMPONENTS AND

IMPLEMENTATION

Lets start by discussing the hardware compotents used

in the work.

2.1 Description of the Leap Motion

Controller

Leap Motion is a motion sensing and hand tracking

technology designed to provide precise and intuitive

control of digital devices through natural hand and

ﬁnger movements (Ultraleap, 2023). It utilizes ad-

vanced cameras and infrared sensors to track hand and

ﬁnger movements with remarkable accuracy, offering

a wide ﬁeld of view of approximately 135 degrees.

Operating at up to 150 frames per second, it captures

hand gestures and movements in real time, making it

compatible with both Windows and macOS operating

systems. Leap Motion is equipped with a software

development kit (SDK), enabling developers to inte-

grate hand tracking into various applications, includ-

ing virtual reality, augmented reality, and interactive

design. It ﬁnds applications in ﬁelds such as virtual

reality gaming, medical simulations, architectural de-

sign, and more, enhancing user interaction and im-

mersion in digital environments.

CSEDU 2024 - 16th International Conference on Computer Supported Education

544

2.2 Description of Robot Inventor Kit

The LEGO Robot Inventor 5in1 Mindstorms kit is a

ﬂexible and educational robotics set that allows users

to build and program ﬁve different robots (LEGO

Group, 2023). It offers a wide range of components,

including motors, sensors, and a programmable hub,

enabling users to create robots capable of various

tasks. With the help of the intuitive LEGO Mind-

storms software, users can code their robots to per-

form actions, follow commands, and even respond to

sensors. This kit is designed to teach programming,

engineering, and problem-solving skills in a fun and

hands-on way, making it suitable for both beginners

and more experienced robotics enthusiasts.

2.3 Competitive Solutions

There are many robotics platforms that can provide

an excellent environment to practice artiﬁcial intelli-

gence and robotics algorithms. Among these we have,

for example, the Aduino and the Raspberry Pi. The

advantage of Lego robots over other solutions is that

they have a closed, safe design, are very fault-tolerant

and have good quality sensors and servomotors in re-

lation to their price. In addition, by controlling Lego

robots in a semi autonomous way, serious robotics al-

gorithms can be tested at university level. For these

two reasons alone, we ourselves use these robots in

the process of educating students and decided to use

one of them in our work.

Next, let’s demonstrate how to use our library to

control robots with lego 5in1 robot inventor kits and

gesture recognition system.

2.4 Robot Control Using the Python

Library le mind controller

The library, named le mind controller and avail-

able at the following address as open source soft-

ware: github.com/wcyb/le mind controller, enables

communication and control of the hub, a key com-

ponent of the Lego Mindstorms set numbered 51515.

In addition, it should also work with the hub from the

Lego Spike Prime set numbered 45678, due to the fact

that the hubs in the two sets differ only in external ap-

pearance, but tests have not been conducted on the

hub from the 45678 set. Connection to the hub can

be made via a USB cable as well as via Bluetooth.

The type of connection does not affect the operation

and use of the library. The library is divided into four

modules:

1. Helpers.py - contains helper functions, respon-

sible for listing the serial ports available on

the system and for establishing a connection

through the selected port. The open source py-

Serial library, available at the following address:

github.com/pyserial/pyserial, is responsible for

the technical, operating system-dependent aspects

of handling serial ports.

2. MindComm.py - is responsible for formatting and

sending control commands to the hub. It also re-

ceives responses and data sent by the hub, and

then directs them to a parsing function in another

module. When sending commands, it is important

to remember that each must contain an individual

identiﬁer. It is randomly generated, has a length of

four characters and consists of uppercase and low-

ercase letters, numbers and ”- ” characters. When

the hub executes a command, it sends back a mes-

sage with the same identiﬁer as the command sent.

This makes it easy to control the status of com-

mand execution.

3. MindData.py - processes the data received from

the hub, as well as contains the deﬁnition of con-

stant values used by the modules connected to the

hub and the hub itself, such as the color seen by

the sensor or the type of module connected. The

functions contained in this module allow to eas-

ily obtain the information of interest from the hub

itself as well as the modules connected to it.

4. SerComm.py - it is used to handle events related

to the connection with the hub. Here one can ﬁnd,

among others, functions called in case of connec-

tion loss or receiving a new line of data from the

hub.

2.5 Gesture Recognition Based on Leap

Motion

The code snippet in Figure 2 is an implementation for

handling data from the Leap Motion device in Python.

In brief, this code analyzes data related to hand and

wrist movements provided by the device and identi-

ﬁes certain gestures based on that data.

The rotation angles (roll) and directions (yaw,

pitch) are transformed from radians to degrees. Then,

the code checks the type of hand (left or right) and

performs different conditional checks based on the

range of the hand’s rotation angle.

The main gestures identiﬁed by the code include

wrist movements, hand tilt angles, and the detection

of a ﬁst by analyzing the ﬁnger angle. The results are

then sent to the console and logged to a ﬁle named

”log.txt” in the form of gesture labels and angle val-

ues.

Prototyping Educational and Scientiﬁc Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap

Motion Integration Case Study

545

rolldeg = normal.roll * Leap.RAD_TO_DEG

arm = hand.arm

content1 = ""

content2 = ""

if handtype == "Right hand":

if -125 <= rolldeg <= -45:

content = "gestp:2"

armdeg = arm.direction * Leap.RAD_TO_DEG

handdir = direction.yaw * Leap.RAD_TO_DEG

- armdeg[0],→

if -15 < handdir < 15:

content1 = "gest:3"

content2 = handdir

#gest left, driving left

elif handdir < -15:

content1 = "gest:4"

content2 = handdir

#gest right, driving right

elif handdir > 15:

content1 = "gest:5"

content2 = handdir

elif -45 <= rolldeg <= 45:

armdeg = arm.direction * Leap.RAD_TO_DEG

handdeg = direction.pitch *

Leap.RAD_TO_DEG - armdeg[1],→

content = "gestp:1"

finger = hand.fingers[2]

bone = finger.bone(3)

punch = - bone.direction[1] *

Leap.RAD_TO_DEG - direction.pitch *

Leap.RAD_TO_DEG

,→

if 5 < handdeg < 35:

content1 = "gest:6"

content2 = handdeg

#gest down, driving backward

elif handdeg < 5:

content1 = "gest:7"

content2 = handdeg

#gest up, driving forward

elif handdeg > 35:

content1 = "gest:8"

content2 = handdeg

#reset or exit gest

if punch < -40:

content1 = "gest:0"

content2 = punch

else:

print "gest not found"

content = False

if content:

print content, "\n", content1, "\n",

"degvalue:", content2,→

with open("log.txt", "a") as file:

file.write(content + "\n")

% \end{lstlisting}

Figure 2: Gesture recognition based on data from leap mo-

tion.

The gestures used in the work can be seen in Figs.

3 through 10.

Figure 3: Gest right, driving right.

Figure 4: Gest right, driving right.

Figure 5: Gest left, driving left.

Figure 6: Gest left, driving left.

Figure 7: Gest up, driving forward.

2.6 Controlling the Robot with Gestures

The provided code snippet is responsible for receiving

data from a process, analyzing the received messages

regarding types of gestures or angle values, and taking

appropriate actions based on the received commands.

This code is dedicated to interacting with a device,

CSEDU 2024 - 16th International Conference on Computer Supported Education

546

Figure 8: Gest up, driving forward.

Figure 9: Gest down, driving backward.

Figure 10: Gest down, driving backward.

likely related to movement or steering a vehicle.

In the code, each received line is processed to ex-

tract gesture type or angle value. Depending on the

identiﬁed gesture, corresponding actions are taken,

such as controlling motors to move in different direc-

tions.

The code involves checking and comparing the

received commands, performing speciﬁc actions for

each command, and controlling motors accordingly.

The overall functionality is geared towards the con-

trol and movement of a device based on gestures and

angles received from an external source.

2.7 Demonstration and Codes

For the project, we used a robot control library devel-

oped in our scientiﬁc team (Cybowski, 2023), based

on the intelligent lego cube from the Robot Inven-

tor kit (LEGO Group, 2023). And we used the SDK

of the LeapMotion hand tracking device (Ultraleap,

2023). A demonstration of how to use the system is

available at the youtube link (Malinowski and Artiem-

jew, 2023). Screen shots of the gestures used can be

seen in ﬁgures from 3 to 10. The codes applied to the

controls in Figures 2, 11 and 12. An example robot

process = subprocess.Popen(

[python2_path, "./Development/Sample.py"],

stdin=subprocess.PIPE,

stdout=subprocess.PIPE,

stderr=subprocess.PIPE,

universal_newlines=True)

[...]

for line in process.stdout:

if line != "\n":

# Divide the line by gesture type or

angle value,→

line_gest = line.split(":", 1)

if line_gest[0] == "gest":

command = line_gest[1]

if command:

print("Command:", command, end="")

# Check the order received and take

appropriate action,→

# gest up, driving forward

if command == '8 \n':

#Taking action on change of command

if last_command and last_command != '8

\n':,→

print('up')

mc.cmd_stop_program_execution()

mc.cmd_motor_turn_on(HubPortName.A,

-50),→

mc.cmd_motor_turn_on(HubPortName.E,

50),→

status = True

# gest down, driving backward

elif command == '7 \n':

if last_command and last_command != '7

\n':,→

print('down')

mc.cmd_stop_program_execution()

mc.cmd_motor_turn_on(HubPortName.A,

50),→

mc.cmd_motor_turn_on(HubPortName.E,

-50),→

status = True

# gest left, driving left

elif command == '4 \n':

if last_command and last_command != '4

\n':,→

print('left')

mc.cmd_stop_program_execution()

mc.cmd_motor_turn_on(HubPortName.A,

50),→

mc.cmd_motor_turn_on(HubPortName.E,

50),→

status = True

Figure 11: Robot control using leap motion data - code

snipet part1.

from the Robot Inventor kit in ﬁg. 13.

2.8 Possibilities for Other Experiments

To expand the awareness of how our system can be

used we will have a discussion in this section about

Prototyping Educational and Scientiﬁc Devices with a Custom Python Library for Lego Robot Inventor 5in1 Mindstorms Kit: A Leap

Motion Integration Case Study

547

# gest right, driving right

elif command == '5 \n':

if last_command and last_command != '5

\n':,→

print('right')

mc.cmd_stop_program_execution()

mc.cmd_motor_turn_on(HubPortName.A,

-50),→

mc.cmd_motor_turn_on(HubPortName.E,

-50),→

status = True

else:

if status:

# Stopping the execution of a

command,→

mc.cmd_stop_program_execution()

status = False

last_command = command

if line_gest[0] == "degvalue":

value = line_gest[1]

if value:

print("value:", value)

# Additional safety shutdown

if not q.empty():

item = q.get()

process.communicate("\n")

sys.exit()

% \end{lstlisting}

Figure 12: Robot control using leap motion data - code

snipet part2.

Figure 13: The robot used in the experimental part - based

on Smart Element Hub cube with LED screen of the Robot

Inventor kit.

possible experimental extensions that we use in prac-

tice with other libraries. First and foremost, in the

lego set under consideration we have the ability to

control servo motors, determine direction of move-

ment, track coloured objects and measure distance to

obstacles. Which, in a student learning environment,

provides a simple way to test localization ﬁlters us-

ing distance in map ﬁelds and distinguishable colour

shades on the ﬂoor. Another area of application is the

implementation and testing of PID controllers for the

precise movement of robots on a map between pre-

deﬁned path co-ordinates, and thus also the testing

of path planning algorithms such as D* Lite. Us-

ing motor speed control and information about the

robot’s position relative to the target direction be-

tween points. In addition, it is possible to test the

algorithms with limited knowledge of the surround-

ings, e.g. by tracking obstacles encountered and map-

ping the terrain. In fact, it is only in the ﬁeld of au-

tonomous robots that it is possible to learn and practi-

cally test dozens of algorithms with this simple robot.

Not to mention the myriad applications creating hy-

brid control systems - where we have only shown one

of them in our publication.

3 CONCLUSION & FUTURE

WORKS

In this paper we presented our new python library

for controlling devices built from the lego robot 5in1

mindstorms kit. That is, a toolkit for testing robotics

and AI algorithms in constructed within the capabil-

ities of this technology. We reviewed possible appli-

cations of the library in education and research. As

a case study, we made a presentation of the combina-

tion of a Leap Motion device for gesture control of an

example robot from the kit. As a demonstrator, we

created a fully functional gesture control system for a

selected robot. In this work, we offer a highly func-

tional tool for prototyping research scenarios related

to the application of robotics and AI techniques in the

IoT area. And we demonstrate an in-house tool for

learning to develop algorithms - with observation of

how they work in practice. In future work, we plan to

demonstrate the possibility of using our library with

other controllers, e.g. the cyberith virtualiser and the

xtion-pro controller.

ACKNOWLEDGEMENTS

This work has been supported by the grant from Min-

istry of Science and Higher Education of the Republic

of Poland under the project number 23.610.007-110

CSEDU 2024 - 16th International Conference on Computer Supported Education

548

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