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Tørresen, Jim
(2023).
Future Intelligent and Adaptive Robots in Real-World Environments.
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Tørresen, Jim
(2023).
Human Intuition and its Impact on Human–Robot Interaction Regarding Safety and Accountability.
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Tørresen, Jim
(2023).
Artificial Intelligence – diverse in methods and applications.
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Tørresen, Jim
(2023).
Robots-assistants that Care about Privacy, Security and Safety.
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Tørresen, Jim; Saplacan, Diana & Mahler, Tobias
(2023).
Ethical, Legal and User Perspectives on Social and Assistive Robots (ELUPSAR) workshop.
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Tørresen, Jim
(2023).
From Adapting Robot Body and Control Using Rapid-Prototyping to Human–Robot Interaction with TIAGo.
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Tørresen, Jim & Yao, Xin
(2023).
Tutorial: Ethical Risks and Challenges of Computational Intelligence.
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Keeley, Crocket & Tørresen, Jim
(2023).
Workshop on Ethical Challenges within Artificial Intelligence - From Principles to Practice.
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Tørresen, Jim
(2023).
What is Robotics?
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Tørresen, Jim
(2023).
What is AI?
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Tørresen, Jim
(2023).
From Adapting Robot Body and Control to Human–Robot Interaction.
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Karbasi, Seyed Mojtaba; Jensenius, Alexander Refsum; Godøy, Rolf Inge & Tørresen, Jim
(2023).
Exploring Emerging Drumming Patterns in a Chaotic Dynamical System using ZRob.
Show summary
ZRob is a robotic system designed for playing a snare drum. The robot is constructed with a passive flexible spring-based joint inspired by the human hand. This paper describes a study exploring rhythmic patterns by exploiting the chaotic dynamics of two ZRobs. In the experiment, we explored the control configurations of each arm by trying to create un- predictable patterns. Over 200 samples have been recorded and analyzed. We show how the chaotic dynamics of ZRob can be used for creating new drumming patterns.
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Saplacan, Diana; Pajalic, Zada & Tørresen, Jim
(2023).
Should Social and Assistive Robots Integrated within Home- and Healthcare Services Be Universally Designed?
Cambridge Handbook on Law, Policy, and Regulations for Human-Robot Interaction.
Cambridge University Press.
ISSN 000-0-000-00000-0.
doi:
ISBN%209781009386661.
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Pajalic, Zada; Saplacan, Diana; Tørresen, Jim & Resa Irai, Hamid
(2022).
Debatt om roboter i helsetjenesten
Bør roboter delta i omsorgsoppgaver? .
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Tørresen, Jim & Yao, Xin
(2022).
Tutorial: Ethical Challenges and Opportunities within Computational Intelligence System Development.
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Tørresen, Jim
(2022).
Robotikk: Digitalisering av fysiske oppgaver.
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Castro da Silva, Bruno & Tørresen, Jim
(2022).
How to write a scientific paper.
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Castro da Silva, Bruno & Tørresen, Jim
(2022).
Deep Reinforcement Learning.
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Maffei, Renan & Tørresen, Jim
(2022).
Mobile Robotics.
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Tørresen, Jim
(2022).
Introduction to Research Visit to University of Oslo in Norway.
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Tørresen, Jim
(2022).
Introduction to Research Visit to University of Oslo in Norway.
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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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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.
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Tørresen, Jim
(2022).
Tutorial: Ethical Challenges in Computational Intelligence Research.
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Tørresen, Jim & Nakasawa, Atsushi
(2022).
Tutorial: Ethical Considerations in User Modeling and Personalization (ECUMAP).
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Tørresen, Jim
(2022).
Keynote: Multi-Modal Sensing for Care Robots for Older People.
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Tørresen, Jim
(2022).
Tutorial: Ethical challenges for Autonomous and Multiagent Systems.
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Tørresen, Jim
(2022).
Research Ethics in AI and Robotics.
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Tørresen, Jim
(2022).
Ethical Perspectives of Robotics and AI – How to develop preferable system?
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Tørresen, Jim
(2022).
Research on health-related treatment and care technology – A technical and ethical view.
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Tørresen, Jim
(2022).
Sensing, acting and adapting in the real world.
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Saplacan, Diana; Baselizadeh, Adel & Tørresen, Jim
(2022).
Robot Demonstration: Meet TIAGo the robot! The robot showcases several tasks: brushing hair, putting the lipstick on, using a (plastic) knife, moving an object, carrying a bag.
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Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia
[Show all 11 contributors for this article]
(2022).
Direct brain recordings reveal continuous encoding of structure in random stimuli.
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Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia
[Show all 11 contributors for this article]
(2022).
Direct brain recordings reveal continuous encoding of structure in random stimuli.
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Ruud, Markus Toverud; Sandberg, Tale Hisdal; Tranvaag, Ulrik Johan Vedde; Wallace, Benedikte; Karbasi, Seyed Mojtaba & Tørresen, Jim
(2022).
Reinforcement Learning Based Dance Movement Generation.
Show summary
Generating genuinely creative and novel artifacts with machine learning is still a challenge in the world of computational science. A creative machine learning agent can be beneficial for applications where novel solutions are desired and may also optimize search. Reinforcement Learnings’ (RL) interactive properties can make it an effective tool to investigate these possibilities in creative contexts. This paper shows how a Reinforcement learning-based technique, in combination with Principal Component Analysis (PCA), can be utilized for generating varying movements based on a goal picking policy. The proposed model is trained on a data set of motion capture recordings of dance improvisation. Our study shows that the trained RL agent can learn to pick sequences of dance poses that are coherent, have compound movement, and can resemble dance.
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Karbasi, Seyed Mojtaba; Jensenius, Alexander Refsum; Godøy, Rolf Inge & Tørresen, Jim
(2022).
A Robotic Drummer with a Flexible Joint: the Effect of Passive Impedance on Drumming.
Show summary
Intelligent robots aimed for performing music and playing musical instruments have been developed in recent years. With the advancements in artificial intelligence and robotic systems, new capabilities have been explored in this field. One major aspect of musical robots that can lead to the emergence of creative results is the ability to learn skills autonomously. To make it feasible, it is important to make the robot utilize its maximum potential and mechanical capabilities to play a musical instrument. Furthermore, the robot needs to find the musical possibilities based on the physical properties of the instrument to provide satisfying results. In this work, we introduce a drum robot with certain mechanical specifications and analyze the capabilities of the robot according to the drumming sound results of the robot. The robot has two degrees of freedom, actuated by one quasi direct-drive servo motor. The gripper of the robot features a flexible joint with passive springs which adds complexity to the movements of the drumstick. In a basic experiment, we have looked at the drum roll performance by the robot while changing a few control variables such as frequency and amplitude of the motion. Both single-stroke and double-stroke drum rolls can be performed by the robot by changing the control variables. The effect of the flexible gripper on the drumming results of the robot is the main focus of this study. Additionally, we have divided the control space according to the type of drum rolls. The results of this experiment lay the groundwork for developing an intelligent algorithm for the robot to learn musical patterns by interacting with the drum.
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Castro da Silva, Bruno & Tørresen, Jim
(2021).
How to Write a Scientific Paper.
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Castro da Silva, Bruno & Tørresen, Jim
(2021).
Deep Reinforcement Learning.
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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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Barone, Dante & Tørresen, Jim
(2021).
Some ethics considerations about AI.
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Maffei, Renan; Mantelli, Mathias & Tørresen, Jim
(2021).
Mobile Robotics.
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Iwashita, Yumi; Stoica, Adrian & Tørresen, Jim
(2021).
Sensing for autonomy navigation.
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Lindblom, Diana Saplacan; Tørresen, Jim & Hakimi, Nina
(2024).
Dynamic Dimensions of Safety -
How robot height and velocity affect human-robot interaction: An explorative study on the concept of perceived safety.
University of Oslo.
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Schulz, Trenton; Lindblom , Diana Saplacan; Tørresen, Jim & Badescu, Claudia Andreea
(2024).
Teach Me How To Dance -
An exploration of motion translation methods using the ZTL framework for use with children with autism in educational settings.
University of Oslo.
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Rivå, Ingunn; Hassan, Moder & Tørresen, Jim
(2023).
Towards a High Tempo EMG Interface using Isometric Finger Press Data.
Universitetet i Oslo.
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Strand, Ørjan; Tørresen, Jim & Miura, Jun
(2023).
Capturing and Classifying Complexity for Pedestrian Environments.
Universitetet i Oslo.
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Moghaddam, Emil Engelstad; Miura, Jun & Tørresen, Jim
(2023).
Motion Skill Based Scene Categorisation.
Universitetet i Oslo.
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Naalsund, Julie; Tørresen, Jim & Iwashita, Yumi
(2023).
Multi-Frame Super-Resolution for Enhancing Lunar South Pole Satellite Images.
Universitetet i Oslo.
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Saplacan, Diana; Tørresen, Jim & Sarfraz, Burhan Mohammad
(2023).
Supervisor for the master thesis: "The Privacy-Preserving Capabilities of a Service Robot in a Scenario-based Healthcare Setting - Providing de-identification with face obfuscation through a face recognition system" (Master candidate: Sarfraz, Burhan Mohammad).
Universitetet i Oslo.
Show summary
As the population ages and people’s life expectancy increases, there is a need for more
healthcare personnel. The worrying sign is that the number of people who educate
themselves to become healthcare personnel is predicted to decrease in the coming years.
One countermeasure for the decreasing workforce is introducing service robots into the
healthcare sector. The introduction of service robots in the healthcare sector raises
numerous privacy challenges. There is the question of ensuring service robots follow
current laws and regulations regarding personal data collection and privacy like humans
and the introduction of robots from a well-controlled industrial environment to an
unconstrained healthcare environment populated by humans. This thesis focuses on the
privacy aspect of the Vulnerability in Robot Society project and machine ethics using
the service robot TIAGo from PAL Robotics. The aim is to investigate how a service
robot can provide de-identification for the privacy protection of individuals it encounters
while performing autonomous tasks in an unconstrained healthcare environment. The
proposed privacy-preserving method for de-identification is face obfuscation through
a face recognition system. With the face obfuscation method, the face recognition
system can be considered a privacy-enhancing technology since it tries to enhance the
privacy-preserving capabilities of the service robot. The results from the scenario-based
experiment, which replicates a doctor’s office as an unconstrained healthcare setting,
show that the face recognition system is unreliable with decreased robustness when
multiple real-life conditions, such as pose variation, occlusion and illumination, are
present as they negatively affect the system. Since the face obfuscation method is
interconnected and integrated into the face recognition system, it depends entirely on a
well-functioning face recognition system to function correctly. Thus, it cannot preserve
the privacy of individuals, making the service robot not comply with current laws and
regulations for privacy and correct personal data collection, storage and processing.
This master thesis is part of the Robotics and Intelligent Systems research group, and
the Vulnerability in Robot Society project at the University of Oslo. The Norwegian
Research Council funds the Vulnerability in Robot Society project with project number
288285.
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Noori, Farzan Majeed; Tørresen, Jim; Uddin, Md Zia & Riegler, Michael A.
(2023).
Multimodal Deep Learning Approaches for Human Activity recognition.
Universitetet i Oslo.
ISSN 1501-7710.
Full text in Research Archive
Show summary
Smart homes may be beneficial for people of all ages, but this is especially true for those with care needs, such as the elderly. To assist, monitor for emergencies, and provide companionship for the elderly, a substantial amount of research on human activity recognition systems has been conducted. Several algorithms for activity recognition and prediction of future events have been reported in the scientific literature. However, the majority of published research does not address privacy concerns or employ a variety of ambient sensors.
The objective of this thesis is to contribute to the progress in research relevant to activity recognition systems that use sensors that collect less privacy-related information. The following tasks are included in the work: assessment of sensors while keeping privacy concerns in mind, selection of cutting-edge classification methods, and how to fuse the data from multiple sensors. This thesis contributes to making progress on systems for analyzing human activity and state—or vital signs—for application in a mobile robot.
This dissertation examines two topics. First, it examines the privacy concerns associated with having a robot in the home. On a robot, an ultra-wideband (UWB) radar-based sensor and an RGB camera (for ground truth) were installed. An actigraphy device was also worn by the users for heart rate monitoring. The UWB sensor was selected to maintain privacy while monitoring human activities. Considering different ways to represent data from a single sensor is the second topic under investigation. That is, how data from multiple representations can be combined. For this purpose, we investigate various data representations from a single sensor’s data and analysis using cutting-edge deep learning algorithms.
The contributions provide considerations for equipping a mobile home robot with activity recognition abilities while reducing the amount of privacy-sensitive sensor data. The work also concerns examining the potential privacy restrictions that must be established for the analyzing systems. The thesis contains new methods for combining data from multiple information sources. To achieve our objective, convolutional neural networks and recurrent neural networks were applied and validated using conventional methods.
The conclusion of the thesis is that we can achieve good accuracy with limited sensors while maintaining privacy. It is, however, likely adequate for assisting healthcare personnel and caregivers in their work by indicating current activity status and measuring activity levels, providing alerts about abnormal activities. The results can hopefully contribute to older people being able to live alone in their homes with a larger chance of any unwanted events being quickly detected and notified to the caregivers and providers.
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Olsen, Vincent Coward & Tørresen, Jim
(2022).
Robotic Rehabilitation Aiding with Upper-limb Impairments: Using the Robot TIAGo.
Universitetet i Oslo.
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Tønnessen, Håkon & Tørresen, Jim
(2021).
Thinking Fast and Slow: PINE: Planning and Identifying Neural Network.
Universitetet i Oslo.