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Our publications - ROBIN
ROBIN publication list 2018-2022 (for use by the EVALMIT evaluation).
A more structured sorting of the publications and other dissemination will follow here (to be added):
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Below follows a non-categorised list of publications by ROBIN members.
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Murashova, Natalia; Bergsjø, Leonora Onarheim; Dahl, Heidi Elisabeth Iuell; Omerovic, Aida & Lindblom, Diana Saplacan
(2026).
Ethical risk assessment of AI in practice: a proposal of the methodology for organisational application.
Nordic Machine Intelligence (NMI).
6(4),
p. 43–52.
doi:
10.5617/nmi.13138.
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As Artificial Intelligence (AI) is incorporated into the workflow, several ethical issues and risks which can cause actual harms are being introduced. Despite the well-established frameworks of ethical AI principles, there are few practice-oriented approaches for structured interdisciplinary assessment of the ethical aspects associated with AI use and design. To supply the principles and address the increasing focus on AI-risks from an ethical perspective, an Ethical risk assessmeNt of Ai iN pracTice (ENACT) methodology is proposed. To develop ENACT together with a cross-sectoral, interdisciplinary consortium of Norwegian private and public businesses, the core principles of Design-Based Research (DBR) were applied including real context orientation, collaborative partnership and focus on testing and multiple interactions. Four aspects of the ENACT methodology were collaboratively developed and tested and are proposed in this paper including format, structure, scope and support tools. This paper describes and details three steps of the ENACT methodology and discusses its potential and limitations for qualitative ethical risk assessment of AI in organisational settings.
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Grünbeck, Inger Annett; Kumar, Rahul Prasanna; Teatini, Andrea; Elle, Ole Jakob & Wiig, Ola
(2025).
4D mixed reality tool for orthopaedic surgery - a feasibility study.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization.
ISSN 2168-1163.
13(1).
doi:
10.1080/21681163.2025.2589161.
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Accurately diagnosing joint conditions and bone deformations relying on 2D imaging remains challenging. This paper examined the feasibility of a new workflow for real-time motion visualisation of a patient’s hip joint, using our novel 4D mixed reality tool providing a non-invasive ‘X-ray vision’ experience. A feasibility study was conducted to evaluate the workflow in a clinical context, asking surgeons to locate pain-causing bone deformations during patient examinations, using X-ray, CT, and our tool. Questionnaires with a Likert scale ranging from ‘Strongly Disagree’ to ‘Strongly Agree’ confirmed the clinical feasibility of our workflow. Surgeons highly rated our tool’s visualisation capabilities and assigned the highest marks when asked about their confidence in pinpointing the deformities’ locations (‘Strongly Agree’): X-ray (0%), CT (25%), presented tool (100%). We believe our visualisation tool will become an asset in advancing orthopaedic diagnostics in the future, contributing to improved patient care and surgical outcomes.
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Torresen, Jim; Laschi, Cecilia; Prestes, Edson; Kavraki, Lydia E.; Caleb-Solly, Praminda & Weng, Yueh-Hsuan
(2025).
Robot Ethics: Ethical, Legal, and User Perspectives in the Development and Application of Robotics and Automation [From the Guest Editors].
IEEE robotics & automation magazine.
ISSN 1070-9932.
32(4),
p. 11–11.
doi:
10.1109/mra.2025.3619589.
Full text in Research Archive
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Waarum, Ivar-Kristian; Blomberg, Ann Elisabeth Albright; Krogstad, Thomas Røbekk; Dewar, Marius & Ellefsen, Kai Olav
(2025).
Optimal Sampling Patterns for Robotic Environmental Monitoring.
International Conference on Control, Automation and Robotics (ICCAR).
ISSN 2251-2446.
2025(2025),
p. 163–169.
doi:
10.1109/iccar64901.2025.11073028.
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Cost-efficient methods and technologies for environmental monitoring is an enabler for holistic decision making and sustainable management of natural resources. The information value from monitoring surveys with robotic sensor platforms such as AUVs and UAVs can increase if domain knowledge is exploited when selecting the sampling locations. We investigate how to maximize the information value of surveys targeting dispersible substances by adjusting the survey pattern in accordance with the direction and strength of the wind or water currents. Samples are acquired with a range of different patterns, deployed in a large number of randomly generated emission scenarios in a Monte Carlo framework. The information value from each survey is used as a performance metric to identify opportune pattern configurations. Among the results is the impact that the orientation of the survey pattern relative to the direction of the wind has on the information value, and that given proper orientation the line spacing can be wider. The results are used to suggest guidelines for robotic environmental monitoring surveys.
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Hafting, Yngve & Wold, Alexander
(2025).
Testbench analysis using non-invasive fault injection.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
ISSN 2639-3867.
2025,
p. 1250–1256.
doi:
10.1109/ipdpsw66978.2025.00203.
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Detection and correction of implementation defects in an early stage of development is known to reduce development time and cost. Verification is an integral part of hardware development. Errors in the verification testbench can lead to a reported false higher functional coverage metric and undiscovered defects in the implementation.One technique to address this is to verify the implementation using a validated testbench. However, validated testbenches are typically not available for sub-modules in an implementation. A validated testbench can be obtained in multiple ways, including undertaking systematic verification of its correctness.In the research presented here, we introduce an integrated methodology to validate the testbench prior to functional verification of the implementation. Module output or state is altered using non-invasive fault injection at simulation time. This is done to validate the correctness of the testbench. Examples of how this is implemented are given in both VHDL and Python. This approach results in a testbench where it is demonstrated (through simulation) that design defects will be detected. Furthermore, by ensuring the correctness of the testbench, a higher confidence in functional coverage is achieved.The techniques presented in this paper were implemented in a testbench used to ensure safe operation of a pulse width modulation circuit for motor control in a course assignment. A brief discussion of how and why this was done is presented.
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Saplacan, Diana; Otterdijk, Marieke van & Tørresen, Jim
(2024).
Qualitative Observational Video-Based Study on Perceived Privacy in Social Robots’ Based on Robots Appearances.
Advanced Robotics and its Social Impacts (ARSO).
ISSN 2162-7568.
doi:
10.1109/ARSO60199.2024.10557941.
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Privacy has recently got attention, especially since
the introduction of the General Data Protection Regulation
(GDPR) in Europe, the new Artificial Intelligence Act (AIA)
and the new Machinery Regulation. Privacy can be defined as
someone’s right to keep personal matters, including personal
life, personal information, or relationships, to themselves. A
social robot’s appearance (=the combination of embodiment
and motion) may contribute to how human users perceive
them, including how these robots are perceived in relation to
privacy. If these robots are part of certain services such as
home- or healthcare, these may also have consequences on how
these services are perceived. This study aims at showcasing the
users’ perception of privacy based on the perceived robot’s
appearance. Three social robots were chosen for this purpose:
PLEO (with a zoomorphic appearance), Pepper (with a childlike
anthropomorphic appearance), and TIAGo (with a
mechanical and asymmetrical appearance). The data was
collected through an in-lab observational video-based study
from 50 participants with very limited- or no experience with
robots. Our findings show that PLEO was perceived as
preserving most of the users' privacy, while Pepper was
perceived as more privacy-invasive than PLEO but less than
TIAGo. TIAGo was perceived as hard to interpret in terms of
privacy. Our findings also point out that designing robots with
a cute appearance, such as PLEO, may contribute to
participants trusting the robot more and thus being willing to
share their data. The paper provides a list of characteristics
that participants associated with a social robot as preserving or
not their privacy. Further, the paper discusses the appearance
of these social robots in terms of “cuteness” as a dark pattern
in the design of social robots that may lead to data myopia, but
also the possible consequences this may have, for vulnerable
users, while trying to design more inclusive robots.
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Kwak, Dongho; Baselizadeh, Adel; Otterdijk, Marieke van; Saplacan, Diana & Tørresen, Jim
(2024).
"Noisy" Matching of Motion Velocity of an Assistive Robot to the Users' Walking Velocity.
ACM/IEEE International Conference on Human-Robot Interaction (HRI).
ISSN 2167-2121.
doi:
10.1145/3610978.3640546.
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This study investigates the impact of dynamic matching of robot motion velocity to users' walking velocity in a human-robot approach scenario on three categories: perceived comfort, interactivity, and naturalness. Considering age diversity, participants were divided into two age groups. Young participants tended to rate higher for all three categories when the robot's approaching velocity was dynamically matched. In contrast, elderly participants preferred a steady and slow robot approach for comfort and predictability. These findings contribute to the ongoing effort to design assistive robots that effectively cater to diverse user groups, ultimately enhancing user satisfaction and acceptance. Taken together, this study highlights the importance of dynamically tailoring robot behaviors based on user demographics for positive Human-Robot Interactions.
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Otterdijk, Marieke van; Saplacan, Diana; Laeng, Bruno & Tørresen, Jim
(2024).
An Exploratory Study on People's Intuitive Understanding of Expressive Robot Behavior.
ACM/IEEE International Conference on Human-Robot Interaction (HRI).
ISSN 2167-2121.
p. 1072–1076.
doi:
10.1145/3610978.3640568.
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Robots are anticipated to communicate with humans in our social context. Using nonverbal communication by robots increases communication comprehension because humans intuitively understand such behavior due to their experience of human-human interaction. Thus, this behavior is suitable for studying what makes robot motion intuitive to understand in human-robot interaction (HRI). We study how robot features help humans intuitively grasp expressive robot gestures, concentrating on what makes behavior easy to interpret. After watching eighteen nine-second videos of three robot kinds demonstrating expressive robot actions, 50 participants completed an open-ended survey. Our findings highlight the inputs, mediating factors, and outputs that users reported based on observing examples of expressive robot behavior. These insights are a starting point for analyzing robot behavior from the perspective of intuition and provide a foundation for a theoretical framework for intuition in HRI.
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Veenstra, Frank; Szorkovszky, Alexander & Glette, Kyrre
(2023).
Decentralized Control and Morphological Evolution of 2D Virtual Creatures.
In Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Ed.),
ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference.
MIT Press.
doi:
10.1162/isal_a_00656.
Full text in Research Archive
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Woll, Anita & Tørresen, Jim
(2023).
What is a Smart Hospital? A Review of the Literature.
In Duffy, V.G.; Lehto, M.; Yih, Y. & Proctor, R.W. (Ed.),
Human-Automation Interaction.
Springer.
ISSN 9783031107801.
p. 145–165.
Full text in Research Archive
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Mantelli, Mathias; Noori, Farzan Majeed; Pittol, Diego; Maffei, Renan; Tørresen, Jim & Kolberg, Mariana
(2022).
Semantic Temporal Object Search System Based on Heat Maps.
Journal of Intelligent & Robotic Systems.
ISSN 0921-0296.
106(69).
doi:
10.1007/s10846-022-01760-8.
Full text in Research Archive
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Bordvik, David Andreas; Hou, Jie; Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim
(2022).
Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning,
2022 International Conference on Electronics, Information, and Communication (ICEIC 2022).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781665409353.
doi:
10.1109/ICEIC54506.2022.9748829.
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Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.
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Lopez, Diego A.; Lopez, Manuel A.; Munoz, Dario S.; Santa, Jesus A.; Gomez, David F. & Barone, Dante Augusto Couto
[Show all 8 contributors for this article]
(2022).
Controlling the UR3 Robotic Arm Using a Leap Motion: A Comparative Study.
In Ribei, Paulo Rogério de Almeida; Cota, Vinícius Rosa; Barone, Dante Augusto Couto & Oliveira, Alexandre César Muniz de (Ed.),
Computational Neuroscience: Conference Proceedings Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers.
Springer Nature.
ISSN 9783031084430.
p. 64–77.
doi:
10.1007/978-3-031-08443-0_5.
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Nergård, Katrine Linnea; Endestad, Tor & Tørresen, Jim
(2022).
Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks.
In Ribei, Paulo Rogério de Almeida; Cota, Vinícius Rosa; Barone, Dante Augusto Couto & Oliveira, Alexandre César Muniz de (Ed.),
Computational Neuroscience: Conference Proceedings Third Latin American Workshop, LAWCN 2021, São Luís, Brazil, December 8–10, 2021, Revised Selected Papers.
Springer Nature.
ISSN 9783031084430.
p. 233–249.
doi:
10.1007/978-3-031-08443-0_15.
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Nordmoen, Jørgen; Nygaard, Tønnes Frostad; Samuelsen, Eivind & Glette, Kyrre
(2021).
On Restricting Real-Valued Genotypes in Evolutionary Algorithms.
In Castillo, Pedro A. & Laredo, Juan Luiz Jiménez (Ed.),
Applications of Evolutionary Computation.
Springer.
ISSN 9783030726997.
p. 3–16.
doi:
10.1007/978-3-030-72699-7_1.
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Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks.
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Potel, Pierre; Veenstra, Frank & Glette, Kyrre
(2021).
SoloJam Island : a platform for rhythmic experimentation in spatially segmented multi-agent systems.
In Kantan, Prithvi Ravi; Paisa, Razvan & Willemsen, Silvin (Ed.),
Proceedings of the 2nd Nordic Sound and Music Computing (NordicSMC) Conference.
Aalborg Universitetsforlag.
doi:
10.5281/zenodo.5717860.
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The development of musical robots which create musical performances or assist musicians during their performances has been an active subject of research in the last few years. In this paper we consider a multi-agent system for the improvisation of rhythmic patterns, where agents compete to take the lead and play musical solos. This competing mechanism is based on evolutionary algorithms and auctions. As such a natural evolution of this system, is to investigate the ways in which separating these agents into multiple subpopulations, where some agents occasionally migrate from one subpopulation to the other and try to bring with them rhythmic knowledge of their past experience, affects the musical dynamics of these subgroups. Simulation’s results show that migrating agent’s can disrupt the musical play in those subgroups and ensure diversity in the musical compositions.
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Otterdijk, Marieke van; Neggers, Margot; Tørresen, Jim & Barakova, Emilia Ivanova
(2021).
Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior,
Proceedings of the 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Virtual Conference, July 8-10, 2021.
IEEE Robotics and Automation Society.
ISSN 9781665449526.
doi:
10.1109/ARSO51874.2021.9542833.
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Congruence of verbal and nonverbal behavior of a robot is essential to establish a seamless and natural interaction between humans and robots. We have investigated the preference and user experience of the senior participants that a robot approaches with expressive movement that is tailored or incongruent to the message the robot brings. Twelve elderly experienced three scenarios with different messages varying from good news, bad news, and functional news, accompanied by the corresponding approaching behavior and body posture. The user experience of these elderly was evaluated during these scenarios using a questionnaire and a semi-structured interview. The participants were interviewed about their experience, preference, and motivation for the different robot motion behaviors. The analysis showed no significant difference in user experiences in the good news and functional news scenarios. Also, no clear preference among the different robot behaviors was found. However, a significant preference for congruence in the sad news scenario was found with a clear preference for the sad approaching behavior.
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Nordmoen, Jørgen Halvorsen; Veenstra, Frank; Ellefsen, Kai Olav & Glette, Kyrre
(2021).
MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics.
Frontiers in Robotics and AI.
8.
doi:
10.3389/frobt.2021.639173.
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In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.
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Tariverdi, Abbas; Venkiteswaran, Venkatasubramanian Kalpathy; Richter, Michiel; Elle, Ole Jakob; Tørresen, Jim & Mathiassen, Kim
[Show all 8 contributors for this article]
(2021).
A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.
Frontiers in Robotics and AI.
doi:
10.3389/frobt.2021.631303.
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This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.
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Uddin, Md Zia; Noori, Farzan Majeed & Tørresen, Jim
(2020).
In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning.
In Noori, Farzan Majeed (Eds.),
IEEE 10th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781728173146.
p. 1089–1093.
doi:
10.1109/UkrMW49653.2020.9252708.
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Human behavior analysis in indoor environments from ambient sensor data is one of the most challenging human-machine interaction research topics. In this paper, we present a deep learning-based novel approach for human posture recognition and emergency detection using an ultra-wideband radar sensor. The strength of this sensor is that it collects less privacy-related information than a regular camera. The sensor is installed on a mobile robot to observe a subject from a short distance. At first, raw data is collected from the sensor. Then, the data is used to train a Recurrent Neural Network (RNN) for modeling of different human activities and conditions, including normal and emergency conditions. Finally, the trained model is used for testing the model on new inputs. The experiments were performed using two datasets recorded in lab environments, and the proposed approach produced better recognition performance than the conventional ones. The proposed method can be applied in many prominent research fields (e.g., human-robot interaction) for different practical applications such as mobile robots for eldercare.
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Nygaard, Tønnes; Howard, David & Glette, Kyrre
(2020).
Real world morphological evolution is feasible.
In Coello, Carlos A. Coello (Eds.),
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion.
Association for Computing Machinery (ACM).
ISSN 9781450371278.
p. 1392–1394.
doi:
10.1145/3377929.3398095.
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Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture the natural noise and richness of the real world. Very few of these virtual robots are built as physical robots, and the few that are will rarely be further improved in the actual environment they operate in, limiting the effectiveness of the automatic design process. We utilize our shape-shifting quadruped robot, which allows us to optimize the design in its real-world environment. The robot is able to change the length of its legs during operation, and is robust enough for complex experiments and tasks. We have co-evolved control and morphology in several different scenarios, and have seen that the algorithm is able to exploit the dynamic morphology solely through real-world experiments.
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Saplacan, Diana
(2020).
Situated Ability: A Case from Higher Education on Digital Learning Environments.
In Antona, M. & Stepahnidis, C. (Ed.),
Universal Access in Human-Computer Interaction, Applications and Practice.
Springer Nature.
ISSN 9783030491079.
p. 256–274.
doi:
10.1007/978-3-030-49108-6_19.
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Universal Design (UD) is often associated with disability studies. However, UD is not about disabilities, but about designing for as many people as possible. Traditionally, disability studies are discussed through the lens of medical, relational, social, or socio-relational models. This paper proposes a new salutogenic approach instead, namely the concept of situated ability. Based on the work of Aaron Antonovsky and the salutogenic approach of ease/dis-ease model and his Sense-of-Coherence (SOC) theoretical construct, the paper proposes and discusses situated ability and the ability continuum. Situated ability is sugggested as a form of catalyzing discussions around social equity in a digital society. The proposed concept is supported with examples from an empirical qualitative study on Digital Learning Environments (DLE) used in Higher Education (HE). The empirical data was collected through interviews, and
analyzed using Systematic Text Condensation (STC). The findings are discussed through the lens of the proposed concepts. Finally, the paper argues that a such perspective is needed for maintaining the dignity of others, i.e., the users who experience lower situated abilities when interacting with digital systems; for fostering salutogenic discussions about practice and design that enables users,
without focusing on dedicated solutions for the lower abled, such as assistive technologies; and for social equity in a digital society.
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Jakobsen, Petter; Garcia-Ceja, Enrique; Riegler, Michael; Stabell, Lena Antonsen; Nordgreen, Tine & Tørresen, Jim
[Show all 8 contributors for this article]
(2020).
Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls.
PLOS ONE.
15:e0231995(8),
p. 1–16.
doi:
10.1371/journal.pone.0231995.
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Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.
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Martin, Charles Patrick; Glette, Kyrre; Nygaard, Tønnes & Tørresen, Jim
(2020).
Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning.
Frontiers in Artificial Intelligence.
3(6).
doi:
10.3389/frai.2020.00006.
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Machine-learning models of music often exist outside the worlds of musical performance practice and abstracted from the physical gestures of musicians. In this work, we consider how a recurrent neural network (RNN) model of simple music gestures may be integrated into a physical instrument so that predictions are sonically and physically entwined with the performer's actions. We introduce EMPI, an embodied musical prediction interface that simplifies musical interaction and prediction to just one dimension of continuous input and output. The predictive model is a mixture density RNN trained to estimate the performer's next physical input action and the time at which this will occur. Predictions are represented sonically through synthesized audio, and physically with a motorized output indicator. We use EMPI to investigate how performers understand and exploit different predictive models to make music through a controlled study of performances with different models and levels of physical feedback. We show that while performers often favor a model trained on human-sourced data, they find different musical affordances in models trained on synthetic, and even random, data. Physical representation of predictions seemed to affect the length of performances. This work contributes new understandings of how musicians use generative ML models in real-time performance backed up by experimental evidence. We argue that a constrained musical interface can expose the affordances of embodied predictive interactions.
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Ionescu, Bogdan; Müller, Henning; Péteri, Renaud; Abacha, Asma Ben; Datla, Vivek & Hasan, Sadid A.
[Show all 33 contributors for this article]
(2020).
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
12260,
p. 311–341.
doi:
10.1007/978-3-030-58219-7_22.
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This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs.
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Faitas, Andrei; Baumann, Synne Engdahl; Næss, Torgrim Rudland; Tørresen, Jim & Martin, Charles Patrick
(2019).
Generating Convincing Harmony Parts with Simple Long Short-Term Memory Networks.
In Queiroz, Marcelo & Sedo, Anna Xambo (Ed.),
Proceedings of the International Conference on New Interfaces for Musical Expression.
Universidade Federal do Rio Grande do Sul.
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Generating convincing music via deep neural networks is a challenging problem that shows promise for many applications including interactive musical creation. One part of this challenge is the problem of generating convincing accompaniment parts to a given melody, as could be used in an automatic accompaniment system. Despite much progress in this area, systems that can automatically learn to generate interesting and harmonically plausible accompaniments remain somewhat elusive. In this paper we explore systems where a user provides a sequence of notes, and a neural network model responds with an accompanying sequence of equal length. We consider two popular sequenceto- sequence models; one featuring standard unidirectional long short-term memory (LSTM) architecture, and the other featuring bidirectional LSTM. These are evaluated and compared via a qualitative study that features 106 respondents listening to eight random samples from our set of generated music, as well as two human samples. From the results we see a preference for the sequences generated by the bidirectional model as well as an indication that these sequences sound more human.
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Wang, Congcong; Mohammed, Ahmed Kedir; Cheikh, Faouzi Alaya; Beghdadi, Azeddine & Elle, Ole Jacob
(2019).
Multiscale deep desmoking for laparoscopic surgery.
Progress in Biomedical Optics and Imaging.
ISSN 1605-7422.
10949:109491Y,
p. 1–9.
doi:
10.1117/12.2507822.
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In minimally invasive surgery, smoke generated by such as electrocautery and laser ablation deteriorates image quality severely. This creates discomfortable view for the surgeon which may increase surgical risk and degrade the performance of computer assisted surgery algorithms such as segmentation, reconstruction, tracking, etc. Therefore, real-time smoke removal is required to keep a clear field of view. In this paper, we propose a real-time smoke removal approach based on Convolutional Neural Network (CNN). An encoder-decoder architecture with Laplacian image pyramid decomposition input strategy is proposed. This is an end-to-end network which takes the smoke image and its Laplacian image pyramid decomposition as inputs, and outputs a smoke free image directly without relying on any physical models or estimation of intermediate parameters. This design can be further embedded to deep learning based follow-up image guided surgery processes such as segmentation and tracking tasks easily. A dataset with synthetic smoke images generated from Blender and Adobe Photoshop is employed for training the network. The result is evaluated quantitatively on synthetic images and qualitatively on a laparoscopic dataset degraded with real smoke. Our proposed method can eliminate smoke effectively while preserving the original colors and reaches 26 fps for a video of size 512 × 512 on our training machine. The obtained results not only demonstrate the efficiency and effectiveness of the proposed CNN structure, but also prove the potency of training the network on synthetic dataset.
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Næss, Torgrim Rudland & Martin, Charles Patrick
(2019).
A Physical Intelligent Instrument using Recurrent Neural Networks.
In Queiroz, Marcelo & Sedo, Anna Xambo (Ed.),
Proceedings of the International Conference on New Interfaces for Musical Expression.
Universidade Federal do Rio Grande do Sul.
p. 79–82.
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This paper describes a new intelligent interactive instrument, based on an embedded computing platform, where deep neural networks are applied to interactive music generation. Even though using neural networks for music composition is not uncommon, a lot of these models tend to not support any form of user interaction. We introduce a self-contained intelligent instrument using generative models, with support for real-time interaction where the user can adjust high-level parameters to modify the music generated by the instrument. We describe the technical details of our generative model and discuss the experience of using the system as part of musical performance.
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Martin, Charles Patrick & Tørresen, Jim
(2019).
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks.
In Queiroz, Marcelo & Sedo, Anna Xambo (Ed.),
Proceedings of the International Conference on New Interfaces for Musical Expression.
Universidade Federal do Rio Grande do Sul.
p. 260–265.
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This paper is about creating digital musical instruments where a predictive neural network model is integrated into the interactive system. Rather than predicting symbolic music (e.g., MIDI notes), we suggest that predicting future control data from the user and precise temporal information can lead to new and interesting interactive possibilities. We propose that a mixture density recurrent neural network (MDRNN) is an appropriate model for this task. The predictions can be used to fill-in control data when the user stops performing, or as a kind of filter on the user's own input. We present an interactive MDRNN prediction server that allows rapid prototyping of new NIMEs featuring predictive musical interaction by recording datasets, training MDRNN models, and experimenting with interaction modes. We illustrate our system with several example NIMEs applying this idea. Our evaluation shows that real-time predictive interaction is viable even on single-board computers and that small models are appropriate for small datasets.
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Bråthen, Heidi; Maartmann-Moe, Harald & Schulz, Trenton Wade
(2019).
The Role of Physical Prototyping in Participatory Design with Older Adults.
In Kokil, Uttam & Ota, Tomoko (Ed.),
ACHI 2019, The Twelfth International Conference on Advances in Computer-Human Interactions.
International Academy, Research and Industry Association (IARIA).
ISSN 9781612086866.
p. 141–146.
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How can older adults actively participate in the design processes of assistive robots designed for their homes? We have organized workshops with a group of older adults who worked actively with materials and physical prototyping to design a fetch robot for the home. We present the basics of the workshop materials, how the workshop was performed, and findings on the role of physical prototyping from the workshops.
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Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Ellefsen, Kai Olav; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing,
Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019.
Springer.
ISSN 9783030356644.
p. 58–68.
doi:
10.1007/978-3-030-35664-4_6.
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Casagrande, Flavia Dias; Tørresen, Jim & Zouganeli, Evi
(2019).
Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults.
IEEE Access.
7(1),
p. 111012-–111029.
doi:
10.1109/ACCESS.2019.2933994.
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We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each – including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment.
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Ellefsen, Kai Olav & Tørresen, Jim
(2019).
Self-adapting Goals Allow Transfer of Predictive Models to New Tasks,
Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019.
Springer.
ISSN 9783030356644.
p. 28–39.
doi:
10.1007/978-3-030-35664-4_3.
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Ellefsen, Kai Olav; Huizinga, Joost & Tørresen, Jim
(2019).
Guiding Neuroevolution with Structural Objectives.
Evolutionary Computation.
ISSN 1063-6560.
28(1),
p. 115–140.
doi:
10.1162/evco_a_00250.
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The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from objectives aiming to make networks more modular, such structural objectives have not been widely explored. We propose two new structural objectives and test their ability to guide evolving neural networks on two problems which can benefit from decomposition into subtasks. The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. Intuitively, this should be a powerful guiding target for problems where human users can easily identify a structure. The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. This results in exploration of many different ways to decompose a problem, allowing evolution to find good decompositions faster. Tests on our target problems reveal that both methods perform well on a problem with a very clear and decomposable structure. However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives—and this technique can even increase performance on problems without any decomposable structure at all.
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Krogh, Magnus Reinsfelt; Halvorsen, Per Steinar; Elle, Ole Jacob; Bergsland, Jacob & Remme, Espen W.
(2019).
Dynamic gravity compensation does not increase detection of myocardial ischemia in combined accelerometer and gyro sensor measurements.
Scientific Reports.
9(1).
doi:
10.1038/s41598-018-35630-x.
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Previous studies have shown that miniaturised accelerometers can be used to monitor cardiac function and automatically detect ischemic events. However, accelerometers cannot differentiate between acceleration due to motion and acceleration due to gravity. Gravity filtering is essential for accurate integration of acceleration to yield velocity and displacement. Heart motion is cyclic and mean acceleration over time is zero. Thus, static gravity filtering is performed by subtracting mean acceleration. However, the heart rotates during the cycle, the gravity component is therefore not constant, resulting in overestimation of motion by static filtering. Accurate motion can be calculated using dynamic gravity filtering by a combined gyro and accelerometer. In an animal model, we investigated whether increased accuracy using dynamic filtering, compared to using static filtering, would enhance the ability to detect ischemia. Additionally, we investigated how well the gyro alone could detect ischemia based on the heart’s rotation. Dynamic filtering tended towards lower sensitivity and specificity, using receiver operating characteristics analysis, for ischemia-detection compared to static filtering (area under the curve (AUC): 0.83 vs 0.93, p = 0.125). The time-varying gravity component indirectly reflects the heart’s rotation. Hence, static filtering has the advantage of indirectly including rotation, which alone demonstrated excellent sensitivity to ischemia (AUC = 0.98).
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Casagrande, Flavia Dias; Tørresen, Jim & Zouganeli, Evi
(2019).
Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
11730 LNCS,
p. 462–473.
doi:
10.1007/978-3-030-30490-4_37.
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Khaksar, Weria; Uddin, Md Zia & Tørresen, Jim
(2019).
Multi-Query Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps.
International Journal of Applied Mathematics and Computer Science.
ISSN 1641-876X.
29(4),
p. 641–654.
doi:
10.2478/amcs-2019-0047.
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Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.
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Khaksar, Weria; Uddin, Md Zia & Tørresen, Jim
(2019).
Fuzzy Motion Planning for Nonholonomic Mobile Robot Navigation in Unknown Indoor Environments.
International Journal of Mechanical Engineering and Robotics Research (IJMERR).
ISSN 2278-0149.
8(1),
p. 6–11.
doi:
10.18178/ijmerr.8.1.6-11.
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Rokkones, Anders Skibeli; Uddin, Md Zia & Tørresen, Jim
(2019).
Facial Expression Recognition Using Robust Local
Directional Strength Pattern Features and Recurrent
Neural Network,
9TH IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781728127453.
p. 283–288.
doi:
10.1109/ICCE-Berlin47944.2019.8966234.
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This work proposes a novel facial expression recognition approach to contribute to better human-machine interactions. To do that, edge features in facial expression images are combined with a recurrent neural network (RNN) to classify different facial expressions. Robust edge features are first obtained by using Local Directional Strength Pattern (LDSP) and applied with RNN. This LDSP-RNN approach achieves superior recognition performance than other conventional approaches on a randomly distributed training and testing datasets obtained from a public dataset. The proposed approach should be useful for various practical applications such as a robot analyzing and understanding different human emotions from facial expressions based on robotic vision.
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Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim
(2019).
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN.
IEEE International Conference on Robotics and Automation (ICRA).
ISSN 1050-4729.
2019-May,
p. 8883–8889.
doi:
10.1109/ICRA.2019.8794077.
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Collaborative robots are becoming more common on factory floors as well as regular environments, however, their safety still is not a fully solved issue. Collision detection does not always perform as expected and collision avoidance is still an active research area. Collision avoidance works well for fixed robot-camera setups, however, if they are shifted around, Eye-to-Hand calibration becomes invalid making it difficult to accurately run many of the existing collision avoidance algorithms. We approach the problem by presenting a stand-alone system capable of detecting the robot and estimating its position, including individual joints, by using a simple 2D colour image as an input, where no Eye-to-Hand calibration is needed. As an extension of previous work, a two-stage transfer learning approach is used to re-train a multi-objective convolutional neural network (CNN) to allow it to be used with heterogeneous robot arms. Our method is capable of detecting the robot in real-time and new robot types can be added by having significantly smaller training datasets compared to the requirements of a fully trained network. We present data collection approach, the structure of the multi-objective CNN, the two-stage transfer learning training and test results by using real robots from Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible application areas of our method together with the possible improvements.
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Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing.
IEEE International Conference on Robotics and Automation (ICRA).
ISSN 1050-4729.
2019-May,
p. 9446–9452.
doi:
10.1109/ICRA.2019.8793663.
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If robots are to become ubiquitous, they will need to be able to adapt to complex and dynamic environments. Robots that can adapt their bodies while deployed might be flexible and robust enough to meet this challenge. Previous work on dynamic robot morphology has focused on simulation, combining simple modules, or switching between locomotion modes. Here, we present an alternative approach: a self-reconfigurable morphology that allows a single four-legged robot to actively adapt the length of its legs to different environments. We report the design of our robot, as well as the results of a study that verifies the performance impact of self-reconfiguration. This study compares three different control and morphology pairs under different levels of servo supply voltage in the lab. We also performed preliminary tests in different uncontrolled outdoor environments to see if changes to the external environment supports our findings in the lab. Our results show better performance with an adaptable body, lending evidence to the value of self-reconfiguration for quadruped robots.
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Weber, Aline; Alegre, Lucas N.; Tørresen, Jim & Silva, Bruno Castro da
(2019).
Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise.
In Visi, Federico (Eds.),
Music Proceedings of the International Conference on New Interfaces for Musical Expression.
Universidade Federal do Rio Grande do Sul.
p. 174–179.
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We introduce a machine learning technique to autonomously generate novel melodies that are variations of an arbitrary base melody. These are produced by a neural network that ensures that (with high probability) the melodic and rhythmic structure of the new melody is consistent with a given set of sample songs. We train a Variational Autoencoder network to identify a low-dimensional set of variables that allows for the compression and representation of sample songs. By perturbing these variables with Perlin Noise— a temporally-consistent parameterized noise function—it is possible to generate smoothly-changing novel melodies. We show that (1) by regulating the amount of noise, one can specify how much of the base song will be preserved; and (2) there is a direct correlation between the noise signal and the differences between the statistical properties of novel melodies and the original one. Users can interpret the controllable noise as a type of “creativity knob”: the higher it is, the more leeway the network has to generate significantly different melodies. We present a physical prototype that allows musicians to use a keyboard to provide base melodies and to adjust the network’s “creativity knobs” to regulate in real-time the process that proposes new melody ideas.
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Noori, Farzan Majeed; Wallace, Benedikte; Uddin, Md Zia & Tørresen, Jim
(2019).
A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
11482 LNCS,
p. 299–310.
doi:
10.1007/978-3-030-20205-7_25.
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Noori, Farzan Majeed; Uddin, Md Zia & Tørresen, Jim
(2019).
Robot-Care for the Older People: Ethically Justified or Not?
Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781538681299.
p. 43–47.
doi:
10.1109/DEVLRN.2019.8850706.
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With the growing popularity of robotics technology, robots have also been introduced to take care of older people. Robots can assist seniors in their daily life tasks, monitor their health or provide them companionship. Despite the benefits of robots, several ethical concerns arise in elderly-robot interaction. The old people might get lonely due to less human interaction, or they feel less control in their lives. The elderly might lose their personal liberty. In this paper, we try to highlight the ethical concerns and provide some preliminary suggestions which would be helpful to reduce the ethical concerns by using customized systems with proper guidelines and consultation with older people.
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Lan, Qichao; Tørresen, Jim & Jensenius, Alexander Refsum
(2019).
RaveForce: A Deep Reinforcement Learning Environment for Music Generation.
In Barbancho, Isabel; Tardón, Lorenzo J.; Peinado, Alberto & Barbancho, Ana M. (Ed.),
SMC 2019 Proceedings of the 16th Sound & Music Computing Conference.
Society for Sound and Music Computing.
ISSN 9788409085187.
p. 217–222.
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Alam, Md Golam Rabiul; Hassan, Mohammed Mehedi; Uddin, Md Zia; Almogren, Ahmad & Fortino, Giancarlo
(2019).
Autonomic computation offloading in mobile edge for IoT applications.
Future Generation Computer Systems.
ISSN 0167-739X.
90,
p. 149–157.
doi:
10.1016/j.future.2018.07.050.
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Glette, Kyrre; Ellefsen, Kai Olav; Norstein, Emma Stensby & Bruin, Ege de
(2024).
Automatic Design of Robot Bodies and Brains with Evolutionary Algorithms - Tutorial.
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The evolution of robot bodies and brains allows researchers to investigate which building blocks are interesting for evolving Artificial Life, and how controllers and morphologies can be shaped together for automated robot design. This tutorial aims to introduce evolution of robot body and control, and some of the key challenges one faces when doing experiments in Evolutionary Robotics. These include finding good ways to represent robots (genotypic encodings), challenges related to co-optimizing morphology and control, how environments shape body and control, and selecting the right physical substrate for evolving robots.
After introducing these challenges and showing relevant examples from our own and other labs’ research, we will present a short demo of how to run Evolutionary Robotics experiments in practice, with the Unity ML-Agents framework.
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Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre
(2023).
Toward cultures of rhythm in legged robots.
doi:
10.1162/isal_a_00673.
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It is widely thought that sensorimotor synchronization, underpinning cultural domains such as music and dance, played a critical role in the evolution of human sociality. Here, we present virtual legged robots controlled by central pattern generators (CPGs) that evolve to synchronize motion to rhythmic sensory input in real time. Multi-stage, multi-objective evolutionary algorithms were used to maximize flexibility of the CPGs with respect to control parameters, and then to optimize a neural input layer for wide-ranging susceptibility to rhythmic inputs. The evolved CPGs self-organize to accommodate the input sequence over a range of frequencies and patterns while keeping the agents upright. We show how this behaviour can be scaled up to multiple interacting agents, including with differing morphologies, to produce novel behaviours. We then outline how spike timing dependent plasticity can be used for the acquisition of new motor patterns. Finally, taking inspiration from biocultural evolution and cognitive neuroscience, we suggest ways in which real-time social adaptation can play a key role in the evolution of complex social behaviours in robots.
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Tørresen, Jim & Nakasawa, Atsushi
(2022).
Tutorial: Ethical Considerations in User Modeling and Personalization (ECUMAP).
Full text in Research Archive
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Saplacan, Diana; Tørresen, Jim; Weng, Yueh-Hsuan & Li, Phoebe
(2022).
Tutorial: Robots and Society (RO-SO 2022) / Ethical perspectives and technical challenges and opportunities with care robots.
Full text in Research Archive
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Tørresen, Jim & Kwak, Dongho
(2022).
Tutorial: Rhythm in Development and Learning – Similarities and Differences Between Humans and Technology.
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Sajadi, Seyed Mohammad Reza; Mathiassen, Kim; Brun, Henrik & Elle, Ole Jakob
(2022).
Design, Kinematic Modeling, and Validation of a Robotic-Assisted Transesophageal Echocardiography System.
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Transesophageal Echocardiography is a safe and minimally invasive tool to provide high-quality anatomic and hemodynamic information of the heart and its structures. However, the risk of exposure to a significant level of radiation during combined fluoroscopy and TEE-guided examinations and musculoskeletal disorders due to holding the probe for prolonged periods of time are two main shortcomings of using TEE manually. This paper presents the design, kinematic modeling, and validation of a novel 6 DOF robotic-assisted transesophageal echocardiography system to overcome the above mentioned shortcomings. The robotic system has been designed based on a commercially available TEE probe with 5 actuated Degrees of freedom on the TEE handle and an extra DOF to control the imaging plane. The constant curvature model is employed for the forward and inverse kinematic modeling of the robotic system. The kinematic model and the performance of the robotic system are validated with an experimental setup using a 6DOF OptiTrack real-time tracking system.
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Tørresen, Jim; Pettersen, Klas Henning & Goodwin, Morten
(2021).
NORA forklarer kunstig intelligens –
Podcast #6 med Jim Tørresen.
[Internet].
Podcast.
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Tørresen, Jim; Saplacan, Diana; Lintvedt, Mona Naomi; Mahler, Tobias & Fosch-Villaronga, Eduard
(2021).
Tutorial: Ethical and Legal Assessments Related to Robots and Systems.
Full text in Research Archive
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Nordmoen, Jørgen Halvorsen & Fadelli, Ingrid
(2019).
A new method to enable robust locomotion in a quadruped robot.
[Internet].
TechXplore.
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Langhammer, Birgitta; Woll, Anita & Skjønberg, Jeppe
(2019).
The use of Technology to promote activity and communication by the municipality services, examples from an urban area.
Full text in Research Archive
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Ahmadi, Arash; Rosnes, Ida; Blicher, Pernille; Diekmann, Robin; Schuttpelz, Mark & Glette, Kyrre
[Show all 10 contributors for this article]
(2019).
Publisher Correction: Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V (Nature Communications, (2018), 9, 1, (5381), 10.1038/s41467-018-07797-4).
Nature Communications.
10(1).
doi:
10.1038/s41467-019-10070-x.
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Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim
(2019).
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN.
Full text in Research Archive
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Uddin, Md Zia
(2019).
A Thermal Camera-based Activity Recognition Using
Discriminant Skeleton Features and RNN.
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Recognizing human activities from sensor data is one of the key areas of image processing, computer vision, and pattern recognition researches today. The target of human activity recognition (HAR) is usually to detect and analyze distinguished activities from the data acquired via different sensors (e.g. thermal cameras). This work proposes a HAR approach from videos recorded via a thermal camera. Skeletons of human bodies are extracted from thermal frames using an opensource deep convolutional neural network (CNN)-based approach named OpenPose. It is generally applied on videos of typical color cameras. However, this work adopts OpenPose on thermal images to extract useful features so that the HAR system can be deployed in environments with low lights as well. Once skeletons of human silhouettes are obtained from the thermal images, robust spatiotemporal features are extracted followed by discriminant analysis. Finally, the discriminant features are fed into a deep recurrent neural network (RNN) for activity training and recognition. The proposed HAR method can be applied to monitor the users such as elderly in both bright and dark environments to prolong their independent life, unlike other typical color cameras which are generally applied in bright environments.
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Uddin, Md Zia
(2019).
Activity Recognition Using Smartphone Sensors,
Robust Features, and Recurrent Neural Network.
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Nygaard, Tønnes Frostad & Simon, Matt
(2019).
Inside Facebook's New Robotics Lab, Where AI and Machines Friend One Another.
[Journal].
Wired.
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Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing.
Full text in Research Archive
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Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Lessons Learned from Real-World Experiments with
DyRET: the Dynamic Robot for Embodied Testing.
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Nordmoen, Jørgen; Glette, Kyrre & Ellefsen, Kai Olav
(2021).
Enhancing MAP-Elites to overcome challenges in Evolutionary Robotics.
University of Oslo.
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