Fabıo Stroppa

Stroppa, Fabıo

Name Variants

Fabio, Stroppa
S., Fabıo
Stroppa,F.
Stroppa,Fabio
STROPPA, FABIO
Stroppa, F.
STROPPA, Fabıo
S.,Fabio
F. Stroppa
Stroppa, Fabio
Fabıo Stroppa
Stroppa, Fabıo
S., Fabio
Stroppa F.
Stroppa, FABIO
Fabıo STROPPA
FABIO STROPPA

Job Title

Dr. Öğr. Üyesi

Email Address

fabio.stroppa@khas.edu.tr

Main Affiliation

Computer Engineering

Sustainable Development Goals Report Points

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Scholarly Output

14

Articles

6

Citation Count

11

Supervised Theses

0 Scholarly Output Search Results

Now showing 1 - 10 of 14

Citation - Scopus: 0
Gamifying Haptics User Studies: Comparison of Response Times From Smartphone Interfaces
(Institute of Electrical and Electronics Engineers Inc., 2025) Stroppa, Fabıo; Xu, D.; Sen, U.; Yoshida, K.T.; Stroppa, F.; Nunez, C.M.; Sarac, M.; Computer Engineering
Haptics user studies are often restricted to a set, physical location and use methods that do not captivate the user. Applying game design elements can create an entertaining environment and increase user engagement. Using ubiquitous tools, like smartphones, to conduct haptics user studies could allow researchers to access larger participant groups while a gamified approach could facilitate the data collection by making the experiment more enjoyable. To explore this concept, this work presents a gamified version of an existing psychophysical experiment that investigates response time to multisensory cues using a smartphone based on "Whac-A-Mole". We conducted a user study to compare our gamified interface with an existing psychophysical interface with thirteen participants exploring the response time from eighteen combinations of auditory, haptic, and visual stimuli at different levels of intensities and participant preferences for both interfaces. The results demonstrate that the gamified interface successfully captured similar trends in response times and significantly elevated participant enjoyment p < 0.003), but did not result in equivalent response times to the original interface. This work shows the benefits and drawbacks of following a gamification approach when designing haptics user studies and discusses factors and trade-offs to consider when gamifying studies. © 2008-2011 IEEE.
Citation - WoS: 8
Citation - Scopus: 10
Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey
(Mdpi, 2023) Stroppa, Fabıo; Saraç Stroppa, Mine; Yuksel, Huseyin Taner; Akbas, Baris; Sarac, Mine; Mechatronics Engineering; Computer Engineering
Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human-machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords exoskeleton, design, and optimization. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research.
Citation - WoS: 0
Citation - Scopus: 0
Multiple Global Peaks Big Bang-Big Crunch Algorithm for Multimodal Optimization
(Springer Heidelberg, 2025) Stroppa, Fabıo; Astar, Ahmet; Computer Engineering
The main challenge of multimodal optimization problems is identifying multiple peaks with high accuracy in multidimensional search spaces with irregular landscapes. This work proposes the Multiple Global Peaks Big Bang-Big Crunch (MGP-BBBC) algorithm, which addresses the challenge of multimodal optimization problems by introducing a specialized mechanism for each operator. The algorithm expands the Big Bang-Big Crunch algorithm, a state-of-the-art metaheuristic inspired by the universe's evolution. Specifically, MGP-BBBC groups the best individuals of the population into cluster-based centers of mass and then expands them with a progressively lower disturbance to guarantee convergence. During this process, it (i) applies a distance-based filtering to remove unnecessary elites such that the ones on smaller peaks are not lost, (ii) promotes isolated individuals based on their niche count after clustering, and (iii) balances exploration and exploitation during offspring generation to target specific accuracy levels. Experimental results on twenty multimodal benchmark test functions show that MGP-BBBC generally performs better or competitively with respect to other state-of-the-art multimodal optimizers.
Citation - Scopus: 10
Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator
(Institute for Ionics, 2023) Stroppa, F.; Stroppa, Fabıo; Selvaggio, M.; Agharese, N.; Luo, M.; Blumenschein, L.H.; Hawkes, E.W.; Okamura, A.M.; Computer Engineering
Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot’s autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Citation - WoS: 1
Citation - Scopus: 1
Large Monitors Reduce Tracking Error in Robot-Assisted Visual-Motor Tasks
(IEEE, 2022) Stroppa, Fabio; Stroppa, Fabıo; Amirabdollahian, Farshid; Frisoli, Antonio; Computer Engineering
Robot-assisted rehabilitation often makes use of virtual environments to present the therapy tasks. Virtual reality has the ability of providing valuable visual feedback and enjoyable interaction to the patients; therefore, the way they are displayed to users becomes crucial. Is the monitor size an important feature that influences how the task is perceived and thus affects patients' performance? This study on healthy participants investigates the influence of displays in perceiving haptic effects. The participants performed an experiment using an end-effector robot, where they followed a moving target around a trajectory while disturbed by a simulated perturbation and assisted by an adaptive algorithm. The experiment was presented on two different monitors to assess whether a different size affects their performance. Statistically significant results show that the performance achieved with the large monitor features lower error compared to the small monitor, implying that large monitors might be a better solution for rehabilitation with virtual tasks and assistive robots.
Citation - WoS: 7
Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator
(Springer, 2023) Stroppa, Fabıo; Selvaggio, Mario; Agharese, Nathaniel; Luo, Ming; Blumenschein, Laura H.; Hawkes, Elliot W.; Okamura, Allison M.; Computer Engineering
Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot's autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator.
Citation - WoS: 1
Citation - Scopus: 2
The Impact of Evolutionary Computation on Robotic Design: a Case Study With an Underactuated Hand Exoskeleton
(Ieee, 2024) Stroppa, Fabıo; Saraç Stroppa, Mine; Soylemez, Aleyna; Zyada, Mazhar Eid; Sarac, Mine; Stroppa, Fabio; Mechatronics Engineering; Computer Engineering
Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design.
Citation - WoS: 1
Citation - Scopus: 0
Optimizing soft robot design and tracking with and without evolutionary computation: an intensive survey
(Cambridge Univ Press, 2024) Stroppa, Fabıo; Saraç Stroppa, Mine; Batiya, Jana; Baran, Eray; Sarac, Mine; Mechatronics Engineering; Computer Engineering
Soft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords "soft robot," "design," and "optimization." We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.
Citation - Scopus: 2
Optimizing Real-Time Decision-Making in Sensor Networks
(Institute of Electrical and Electronics Engineers Inc., 2023) Yiǧitbaşi,Y.; Stroppa, Fabıo; Stroppa,F.; Badia,L.; Computer Engineering
The rapid integration of digital technologies into physical systems has given rise to cyber-physical systems, where the interaction between the computational and physical components plays a crucial role. This study explores optimal decision-making in event detection and transmission scheduling within cyber-physical systems, emphasizing the crucial aspect of efficient decision-making. We consider the problem of monitoring and reporting about a single event taking place within a finite time window achieving a reward related to the timeliness of the status update. Thus, the objective corresponds to minimizing the age of information between the instant of the event x and the status update time t, with a further penalty for a missed event. The monitoring apparatus decides when to perform the status update without knowing the value of x, but only knowing its statistical distribution. We assume a triangular probability density function for the instant of the event taking place, with a variable average. We provide an analytical derivation of the optimal choice of the status update, highlighting interesting trends, such as the saturation in the value of t as x grows close to the limit of the observation window. This proposed problem and its analytical formalization may serve as a further foundation for the general analysis of optimal monitoring of cyber-physical systems. © 2023 IEEE.
Citation - WoS: 6
Citation - Scopus: 6
Design optimizer for planar soft-growing robot manipulators
(Pergamon-elsevier Science Ltd, 2024) Stroppa, Fabıo; Computer Engineering
Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovations in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delicate/dangerous environments, manipulation of items, or assistance in domestic environments. This work presents a novel approach for design optimization of soft-growing robots, which will be used prior to manufacturing to suggest to engineers - or robot designer enthusiasts - the optimal size of the robot to be built for solving a specific task. The design process is modeled as a multi-objective optimization problem, optimizing the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. The method exploits the advantages of population-based optimization algorithms, in particular evolutionary algorithms, to transform the problem from multi-objective into single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. The proposed method was tested on different tasks to assess its optimality, which showed significant performance in solving the problem: the retrieved designs are short, smooth, and precise at reaching targets. Finally, comparative experiments show that the proposed method works better than the one existing in the literature in terms of precision (14% higher), resource consumption (2% shorter configurations with 4% fewer links), actuation (85% less wavy and undulated configurations), and run time (13% faster).

Stroppa, Fabıo

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Scholarly Output

14

Articles

6

Citation Count

11

Supervised Theses

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