Using iStar to Describe Human-robot Collaborations:
Exploring Different Ways of Goal Model Usage
Jeshwitha Jesus Raja, Marian Daun
Center for Robotics, Technical University of Applied Sciences Würzburg-Schweinfurt, Schweinfurt, Germany
Abstract
In human-robot collaboration, humans and robots work closely together in a manufacturing process.
To ensure proper and efficient execution of the manufacturing process while considering human safety
and damages to the robot and the work product, advanced planning of the collaborative manufacturing
process is important. Goal models can be used already in the early phases to specify and analyze
human-robot collaborations. However, as model-based development along other established software
engineering practices do not belong to the core of roboticists training, guidance is needed for the creation
and usage of goal models for human-robot collaborations. In this paper, we investigate different ways
how goal modeling can be used to specify human-robot collaborations to determine a set of best practices
and recommendations in the future. To do so, we compare the results of two teams, who - after initial
training on goal models - applied goal modeling to specify the same human-robot collaboration system.
The outcome shows that multiple useful ways of using goal modeling for human-robot collaborations
exist, that need to be considered in the future.
Keywords
Goal Model, Human-Robot Collaboration, Assembly process
1. Introduction
Human–Robot Collaboration (HRC) is an emerging development in the field of industrial
and service robotics, integral to the Industry 4.0 strategy [1]. Some production tasks cannot
be automized or not at an acceptable cost. For example, assembly of flexible lines is still a
problematic task for robots. Another example is the automation of small batch sizes, which
is commonly not achieved cost-efficiently [2]. Therefore, humans and robots collaborate, so
that the human takes care of tasks difficult to automate or steps that vary between different
products, while the robot executes the repeating tasks.
However, establishing human-robot collaboration in industrial practice is challenging due
to its safety-criticality. Human and robot collaborate closely on the same work piece, with
partly overlapping movement trajectories. Therefore, early planning and advanced analysis of
human-robot collaborations are needed already in early development stages. Goal modeling
allows for a systematic specification of tasks and allows for early analysis [3]. Particularly,
iStar [4] allows specifying actors, their goals and tasks, and their relationships, and is thus
well-suited to investigate complex collaborative systems [5]. Thus, goal modeling has already
iStar’24: The 17th International i* Workshop, October 28, 2024, Pittsburgh, Pennsylvania, USA
Envelope-Open jeshwitha.jesusraja@study.thws.de (J. Jesus Raja); marian.daun@thws.de (M. Daun)
Orcid 0009-0008-7886-7081 (J. Jesus Raja); 0000-0002-9156-9731 (M. Daun)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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been successfully applied to human-robot collaboration (e.g., [6]). However, roboticists are often
times not familiar with model-based concepts and analyses, as advanced software engineering
is often not part of their core curriculum. Therefore, guidance is needed to support engineers in
developing iStar models of human-robot collaborations.
For all modeling languages, but particularly for modeling used in early development phases,
there exists a multitude of ways how the modeling language can be used, and the model creator
can develop their own style. Therefore, in this paper, we take a look at different ways of
developing iStar goal models for human-robot collaborations using a concrete case system.
Comparing the different modeling approaches and their outcomes shall allow us in the future
to define guidelines for developing goal models for human-robot collaborations.
The paper is structured as follows: Section 2 discusses related work, followed by Section 3.1,
which describes the study setup along with the case example used. Section 3.2 then presents
the results along with Section 3.3 which presents the major findings. This is later evaluated and
discussed in Section 4. This section also concludes the paper.
2. Related Work
Approaches for modeling human-robot collaboration often focus on the human behavior in-
side the collaboration, which is challenging to sketch, and, therefore, often times needs the
combination of modeling approaches from different perspectives [7]. The term human-robot
collaboration, however, subsumes different levels of autonomy and interaction between human
and robot [8]. As a result, modeling human behavior in human-robot collaborations is still a
challenge [9].
Furthermore, these behavior focused approaches typically can be applied at the later stages of
development. In the very early stages, the exact behavior is unknown and rather a proof concept
is needed or narrowing down the expected behavior of human and robot in the collaboration to
be precisely specified later on. An abstract modeling language for the early phases that already
allows analyses is goal modeling [10, 3]. They can be used in requirements engineering to
document the high-level requirements, to reason over fundamental design decisions, and to
identify conflicts [11]. Goal modeling seems particularly fitting for specifying human-robot
collaboration, as here one of the first steps is to set a common goal for the collaboration that
considers human preferences, task knowledge (including objects), and the capabilities of both
the human and the robot [12].
In previous work, we have shown that GRL goal models are an adequate means for modeling
human-robot collaborations in requirements engineering [13], particularly concerning safety
aspects [6]. Therefore, we have proposed a GRL profile for capturing safety aspects of human
robot collaborations [14]. This profile is based on a previous GRL-compliant iStar extension to
model collaborative cyber physical systems [15].
3. Investigating Possible Uses of iStar to Model Human-Robot
Collaborations
3.1. Study Setup
The goal of this study is to investigate different uses of iStar to model human-robot collaborations.
This shall later on serve as foundation for defining best practices and guidelines.
To do so, we recruited robotics students from a sixth semester elective requirements engi-
neering course. As part of the course’s curriculum, iStar and GRL were taught. In addition, our
extensions for modeling collaborative systems, particularly, human robot collaborations were
presented to the students, and also used for the tasks.
The course was split into two groups, which were given the opportunity to create an iStar
model for a human-robot collaboration system for extra credit. As a case example, a collaborative
assembly station was chosen. The existing case system could be observed by the students, and
the responsible engineers were available to answer detailed questions.
3.2. Preliminary Results
3.2.1. Goal Model 1
Figure 1 shows the goal model created by the first team, which emphasizes how the workstation
should be set up. The goal model features one main actor, ‘Overall Workstation,’ which has the
goal of ‘Output Toy Cars.’ This goal can be achieved through the following components: the
task ‘Information and Safety Layer,’ the task ‘Manipulation Tasks,’ the soft goal ‘Task Shall Be
Distributed Based on the Type of Interaction,’ and the goal ‘Design Specified Workspace.’ The
main actor also includes sub-actors: ‘Ulixes A600,’ which is part of the workstation and includes
a projector for displaying instructions, cameras for monitoring, and a depth sensor for the
engineer to notify task completion; ‘UR5e,’ the collaborative robot (cobot); and the ‘Engineer.’
The goal of these sub-actors is to be prepared for executing the assembly process. The main
actor and the sub-actors have different goals that need to be fulfilled.
3.2.2. Goal Model 2
Figure 2 shows the goal model from the second team, which emphasizes more on the production
process. The group also decided to define one main actor and decompose this one into further
actors. The main actor is ‘Toy Car Production with HRC.’ This actor, has only one goal, namely
‘Assembly of Toy Car for Kids in HRC Assembly Line,’ whose fulfillment depends on the goals
of all sub-actors. Note that we do not show the main actor and its boundary line in Figure 2 in
order to improve the clarity of the figure.
As shown in Figure 2, the main actor consists of 5 sub-actors. Namely: ‘Cobot’, ‘Collaboration’,
‘Camera Systems’, ‘Human Operator’ and ‘Safety System’. The main actor has one main goal
that needs to be fulfilled, which is done through the fulfillment of the sub-actors’ goals. The
goals include tasks that need to be performed before the assembly process and during.
Overall
Workspace
+
Output toy cars +
Resources need to be in
working conditions
AND
Information and safety Manipulation tasks Task shall be distributed
layer based on the type of
interaction
AND AND
Design specified
C_WS shall be higher workspace
C_WS shall be a safe Attach projector at
than the H_WS and
environment appropriate location
R_WS
AND
AND
Pick Place Screw Hold
Have adjustable height Lockable wheels
Outline robot Outline human Outline collaborative
Have a planar surface workspace with color workspace with color workspace with color
red green yellow
Ulixes A600 Successful
boot up
process
AND
Computer Pass risk
System wind-up assessment
system
AND
AND
Ensure robot‘s Ability to identify Ability to identify
ability to perform engineer‘s pose robot’s pose
Boot up
Safety document
document
Ensure continuous operation
AND Camera UR5e
Provide data to Maintain continuous Coverage of entire
Projector projector communication with workspace Perform safe
AND
robot Restrict engineer and operation
AND robot from entering
XOR each others workspace AND
Project instructions Project green button for
Inform robot Monitor distance Malfunction-
for engineer in H_WS engineer to indicate Notify robot in Activate ‚emergency
when to continue between engineer free state of Pass risk Stay within coverage
completion of task case of collision ‚Torque stop‘ mechanism if
a task and robot operation assessment of cameras
sensor‘ needed
AND
Pass Recieve periodic Pass self- Notify operator in
inspection maintenance tests case of failure
Depth sensor
Assembly Assembly steps
document AND
Robot Compile provided
Regulate distance Regulate speed
gripper instructions
AND XOR XOR
Without
Perform collaboration,
tasks in With
Control tablet Perfom maintain a Speed up Slow down
R_WS collaboration,
collaborative distance less mantain a
tasks in the than 30cm distance less
C_WS
than 5cm
Engineer
+
Support continuous .
Supervise
operation
assembly process
AND
Stay within the
Turn on computer Perform given Screw driver
coverage of
Pass training system tasks as specified
cameras
AND
Undergo education Perfom collaborative
Accident prevention
on how to use the tasks in the C_WS
Be physically and system
mentally fit
AND AND
Inform cobot to
Activate start task
Do not come in Do not destroy the
emergency stop
contact with the robot or
button in event of
robot workspace
collision Successful completion
of initial task before
the start of next task
Emergency stop button
Figure 1: Goal Model 1
Cobot Cobot Goals
Collaboration
Assisting Human AND
Has CE markings and Operator in assembly Comply VDI/VDE safety
follows EU regulations standard Collaborative Allowed to move in cobot
Clear light curtain
workspace for main and collaborative light
boundary defined
AND assembly curtain boundaries ONLY
Holding parts for
Picking parts for
assembly
Placing parts for
assembly
assembly in
collaboration
?
Human Operator Goals
? The pace of cobot is
adaptable to work pace of
Intermediate breaks for
human operator
Altering gripper
strength based on the
? human operator
AND
part material
Path planning
Clear light curtain Allowed to move in operator
boundary defined and collaborative light curtain
AND boundaries ONLY
Place parts in the slow down while
Follow the same handling large or sharp
right order and
calculated path objects
location
Camera Assembly for chassis
Projecting and
Systems mounting
monitoring the Human
workspace Operator
AND Pior knowledge about
mech., elec, and
Monitoring assembly
Camera
System 1 Camera 2A Projecting Detecting the
operator in the Working Standards
AND AND workspace
Camera 2B
Detecting the Monitoring of the
steps of assembly Mandatory training for process
in the workspace working on shopfloor
Projecting the Error detection
workspace AND AND
boundaries Constant Operator follows EU
Projecting the AND monitoring of
tasks for cobot regulation, IEEE standards Monitoring of the whole
human operator
and operator vitals process in periodic intervals
Collision detection
Mandatory intensive 2
Falling parts Handle problems related to
day training about
detection emergency stopping
safety on shopfloor
Mandatory intensive 1
week training about the
process on the shopfloor
Safety Safe execution of
System assembly HRC
Emergency button
AND within arms reach of
the operator
Emergency
Safe execution of tasks Safe execution of tasks button
The gripper will not
have sharp edges
? by Cobot by human operator
? Completes each
AND assembly step within 2
? AND mins
The cobot stops
Cobot speed is capped
completly if the The operator uses the touch
to 1m/s
operator crosses the Routine safety button to notify the cobotthat
saftey distance assesments of the the next process can begin
system
Maintenance of the Optical distance
cobot every 4 weeks sensor
Figure 2: Goal Model 2
3.3. Major Findings
The important aspects of the assembly process include the two main actors—the human and the
cobot—the monitoring system, and safety. All these aspects are featured in both goal models,
but not in the same way.
When discussing safety, Figure 1 represents it within each sub-actor, showcasing how the
human and the cobot must individually maintain safety aspects. On the other hand, Figure 2
shows safety as a separate actor, encompassing all the safety aspects of both the human and the
cobot. With regard to the monitoring system, both goal models include it as a separate actor
that features cameras for monitoring and a projector for displaying instructions. Despite using
different labels, the representation of the monitoring system remains consistent in both models.
The main focus of the assembly process is the collaboration between the human and the
cobot. Figure 1 illustrates this collaboration through tasks within the individual actors and their
communication via the monitoring system ‘Ulixes A600’. On the other hand, figure 2 shows the
same with the use of a separate actor. The tasks within this actor are either dependent on or
influence tasks from the ‘human operator’ and ‘cobot’ actors.
This demonstrates how the goal model from Team 1 (Figure 1) encompasses all safety and
collaborative aspects within the respective actors, while the goal model from Team 2 (Figure
2) separates safety and collaboration into distinct actors. Regarding the elements used, both
teams incorporate basic goals, tasks, decompositions, and resources. Additionally, Team 2
focuses more on contributions and soft goals. Regardless of the approach taken to create the
goal models, both models represent the collaborative workspace for manufacturing toy cars,
including the human, cobot, monitoring system, and workspace safety.
In conclusion, although the two teams used different approaches to goal modeling for speci-
fying human-robot collaborations, both successfully represented the complete collaboration
and met the intended specifications.
It is well known for modeling that different modelers will end up with different models by
using different modeling elements, modeling at different levels of granularity, or preferring a
different layout. In addition, giving the degrees of freedom, modelers might select a different
focus of a model due to their intended purpose. In our case, we did make specific requirements
regarding the purpose of the modeling, other than that the model should adequately specify the
case example. Therefore, it is not surprising that both models look different, but it shows that
for capturing the important parts of human-robot collaboration multiple aspects are relevant,
which were covered in both models. However, depending on the particular intention, e.g., giving
safety the visual importance of an actor, in contrast to showing how safety plays a vital role
within all actors, these aspects can be treated very differently.
For the future, it remains to investigate further approaches to modeling human-robot col-
laborations with goal models and to analyze the usefulness of the possible approaches for
different purposes. It can particularly be questioned whether a view concept is needed, as
human-robot collaboration deals with a set of very vital aspects that are of importance to
completely understand the collaboration and appropriately specify the system. For instance,
• the production or assembly process is needed as the main context constraint, limiting the
solution space;
• safety must be considered as vital factor to enable real-world application of human-robot
collaborations;
• the physical actors, i.e. the human and the robot, where both need to be given specific
tasks aligning with each other;
• the collaboration itself, as source of constraints for aligning the actions of the human and
the robot;
• monitoring, planning systems, the production systems, aside from the robot itself human-
robot collaborations rely on other technical systems needed.
4. Conclusion
Human-robot collaboration is an evolving field in industrial robotics, to allow for semi-
automation of complex production processes. To ensure successful collaboration in terms
of product quality and safety, advanced planning of the collaboration process is needed. Goal
modeling can aid in the specification and analysis of human-robot collaborations already in the
early phases. However, currently there is a lack of guidance for roboticists on how to create
and use goal models for human-robot collaborations best.
In this paper, we reported a first study to shed light into the use of goal models for human-
robot collaborations. Two teams were tasked with creating iStar goal models for an existing
human-robot collaboration system. The results substantiate the assumption that iStar goal
modeling is applicable and useful in this scenario. Both groups yielded in completely different
models, placing emphasis on different aspects. This, highlights the need for future research to
identify the crucial points on what is most useful to investigate in early requirements engineering
for human-robot collaborations. Furthermore, it might indicate that a view concept is needed
to emphasize multiple aspects of human-robot collaboration.
In addition, since the approach was tailored to a specific human-robot collaboration use case
and applied only to a particular group of engineering students, its generalizability cannot be
assumed. Thus, for future work, it is important to explore more case studies involving a wider
variety of robotic collaborations and different types of interactions.
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