PIRC Predictive and Intuitive Robot Companion

• Markus Yuki Nishimura, Kohei Matsumoto, Jim Tørresen & Ryo Kurazume (2026). FLexible Multi-Robot Social Navigation Using Diffusion-Based Deep Reinforcement Learning. In Kazuo Ishii, Tomohiro Shibata, Ayanna Howard, Silvia Rossi & Aurélie Clodic (Ed.), Proceedings of The 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2026). IEEE. ISSN 9798331588656. p. 2122–2127. doi: 10.1109/RO-MAN72286.2026.11714669. Full text in Research Archive Show summary

  Diffusion-based reinforcement learning has recently shown strong performance in robot navigation, and prior work has demonstrated that Q-Score Matching (QSM) is effective in crowded environments. Building on this line of work, we study inference-time guidance for multi-robot social navigation using Multi-Agent Q-Score Matching (MAQSM) as the backbone policy. Rather than modifying the training objective, the proposed method steers reverse diffusion sampling of a trained MAQSM policy at evaluation time toward more coordinated multi-robot behavior. The guidance combines formation consistency and goal alignment, enabling deploymenttime control without retraining. In our implementation, robots share the same diffusion policy parameters, generate per-robot actions under this shared policy, and execute the resulting multi-robot action jointly in the simulator. Experiments first compare QSM and MAQSM to motivate the choice of MAQSM as the underlying multi-robot backbone, and then compare unguided and guided MAQSM in a more challenging bidirectional scenario. The results show that inference-time guidance improves success and safety in denser settings, at the cost of longer navigation time. These findings position post-training guidance as a practical way to improve diffusion-based multirobot policies while preserving the original learning pipeline.

• Jim Tørresen, Diana Saplacan Lindblom, Adel Baselizadeh, Tobias Mahler & Lee Andrew Bygrave (2026). Reducing Robot Vulnerabilities Through Joint Regulatory Assessment and User-Centered Development and Testing. In Yueh-Hsuan Weng (Eds.), Social Robots and Ethical Design. Springer. ISSN 9789819225156. Full text in Research Archive Show summary

  This book chapter introduces research examples from a unique collaboration between legal scholars and technology scholars who have together endeavored to make progress in safe and privacy-preserving robot assistant development and deployment. This collaboration has taken place within the “Vulnerability in the Robot Society” (VIROS) research project at the University of Oslo, Norway (VIROS 2025). Institutionally, this is a joint research project between the Norwegian Research Center for Computers and Law (NRCCL) at the Department of Private Law and the Robotics and Intelligent Systems (ROBIN) research group at the Department of Informatics. The project explores challenges regarding digital vulnerabilities that the adoption of robots in human environments poses, particularly as regards privacy, safety, and security, and how these challenges can be met from legal and technical perspectives. We have been particularly interested in exploring assistant and healthcare robots since this domain imposes high demands on the technology to be used. As part of the project, we have analyzed various European Union (EU) regulations that have been introduced in recent years, with a view to assessing their impact in the domain. The TIAGo robot from PAL Robotics has been the main robot platform for technical work and experimental studies in the project. A number of novel methods for robot sensing and control have been implemented. Further, several of them have been assessed through human–robot interaction studies. This chapter introduces the VIROS project and outlines some of our regulatory and technological research. We also highlight several of the conflicting trade-offs seen in the domain of HRI (Torresen et al. 2023).

• Diana Saplacan Lindblom, Sanna Kuoppamäki, Leon Bodenhagen & Jim Tørresen (2026). Robots as Welfare Technologies and Actors within Future Home and Healthcare Services. In Laura Havinen & Susanne Hägglund (Ed.), Adjunct Proceedings of the 14th Nordic Conference on Human-Computer Interaction. Association for Computing Machinery (ACM). ISSN 9798400727986. p. 1–4. doi: 10.1145/3821402.3830201. Full text in Research Archive Show summary

  This workshop explores whether, how, and under which conditions Welfare Robots (WRs), may be meaningfully integrated into future home- and healthcare services and practices. Building on current debates on welfare technology, AI-driven robots, and post-growth Human-Robot Interaction (HRI), the workshop invites designers, roboticists, engineers, healthcare professionals, philosophers, sociologists, and other practitioners to critically examine and systematically engage in contributing to exploring design alternatives and co-design a future model for responsible integration of robots into future care services, using Story Dialogue Method.

• Leon Bodenhagen, Pat Treusch, Norbert Kruger & Diana Saplacan Lindblom (2026). Workshop: Robociety – Vision and Reality. In Prof. Johanna Seibt, Prof. Rachid Alami, Prof. Emeritus Raja Chatila, Prof. Emeritus Charles Ess, Prof. Kerstin Fischer & Prof. David Gunkel, (Ed.), Conference Proceedings of the Bi-Annual Conference Robophilosophy - "Connected Futures: Nature, Robots, and Society", 2026. Springer. Full text in Research Archive Show summary

  Robociety refers to the pervasive use of interactive robots in everyday life, which today is still more a vision than a reality. It is also a name of a project [1] with the overarching aim to ”unlock the benefits that robots can bring to society - while proactively addressing the potential risks they pose”. At the workshop, we will discuss the state of the art in interactive robots and their applications in real-world environments. By identifying technical, ethical, and procedural challenges, we aim to pinpoint promising application areas as well as societally meaningful and at the same time commercially viable use cases. These could pave the way for broader integration of interactive robots into daily life.

• Diana Saplacan Lindblom, Yueh-Hsuan Weng, Pedro Pablo Lucas Bravo, Adel Baselizadeh & Jim Tørresen (2026). A Cross-Cultural Video-Based Study between Norway and Japan on Informed Consent and Disclosure Mechanisms on the Use of Social Robots in Public and Private Spaces - Users' Perspectives. In Kazuo Ishii, Tomohiro Shibata, Ayanna Howard, Silvia Rossi & Aurélie Clodic (Ed.), Proceedings of The 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2026). IEEE. ISSN 9798331588656. doi: https:/ieeexplore.ieee.org/document/11714840/. Full text in Research Archive
• Claudia Magdalena Sikora, Marieke van Otterdijk, Rebekka Soma-Jestilä, Jim Tørresen & Diana Saplacan Lindblom (2026). "There’s something human about it!” - Exploring users’ perception of nonverbal communication of robotic furniture. In Hooman Samani & Saina Akhond (Ed.), Proceedings of the International Confernece on Social Robotics (ICSR) + ART 2026. Springer International Publishing . Full text in Research Archive Show summary

  Non-anthropomorphic robots are integrating into our homes. People attribute human-like characteristics to these robots, although they do not resemble humans. This raises the question of whether similar processes would occur when people interact with robot furniture. Thus, the presented exploratory pilot study investigates how users intuitively interpret and perceive the non verbal communication of such robots. In an instrumental case study, we had eleven participants interacting with the T-ABLE robot through expressive behavior. Participants were interviewed about their experience. Our findings indicate that the participants use their associations between behavior and prior to similar experiences to attribute personality to the robot table. These findings provide insights for theorizing the intuitive comprehension of the behavior of non-anthropomorphic robots.

• Burhan Mohammad Sarfraz, Diana Saplacan Lindblom, Adel Baselizadeh & Jim Torresen (2026). The Privacy-Preserving Capabilities of a Service Robot in a Scenario-Based Healthcare Setting. In Lynne Baillie, William D. Smart, Maartje De Graaf, Matthew Gombolay & Ilaria Torre (Ed.), Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI Companion '26). Association for Computing Machinery (ACM). ISSN 9798400723216. p. 675–679. doi: 10.1145/3776734.3794481. Full text in Research Archive
• Tobias Lømo, Adel Baselizadeh, Kai Olav Ellefsen & Jim Tørresen (2026). Dual Process Dreamer: Fast and Slow Decision-Making with World Models. Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART). ISSN 2184-3589. 2, p. 1230–1241. doi: 10.5220/0014243200004052. Full text in Research Archive Show summary

  Most robot systems are based on a single decision-making process. This process needs to balance time, energy, and accuracy in every situation. However, according to ”dual process theory” (DPT) from cognitive psychology, this is not how humans work. Depending on the situation, we have the ability to switch between two thinking methods, a fast system 1 (S1) and a slower system 2 (S2). In this paper, we propose a novel approach to a dual process architecture for robots and agents. Our method, called Dual Process Dreamer (DPDreamer), is a combination of a reinforcement learning policy network, a planning algorithm, and a learned world model. The world model allows the parts of DPDreamer to work together and create a more integrated system compared to previous proposals of DPT systems. DPDreamer was tested in a puzzle game called Sokoban, and by balancing the use of S1 and S2, DPDreamer managed a success rate similar to S2 while using S1 most of the time, showing the benefit of using a more adaptable system.

• David Pich, Adel Baselizadeh, Jim Tørresen & Jun Miura (2025). Privacy-Preserving Machine Learning-Based In-Bed Sleep Posture Monitoring Using Wi-Fi Signals. In Abdelaziz Benallegue (Eds.), 2025 13th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331591410. doi: 10.1109/iccma67641.2025.11369717.
• Geir Paulsen, Juan Sebastian Cardenas, Rosa Nicoline Pham Alsgaard, Adel Baselizadeh, Md Zia Uddin & Jim Tørresen (2025). AI-Based User Gesture Recognition for Human-Robot Interaction Using Wrist Sensors. In Eisuke Kita (Eds.), 2025 11th International Conference on Robotics and Artificial Intelligence (ICRAI), December 19-21, 2025, Nagoya, Japan. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331590680. p. 161–165. doi: 10.1109/icrai68431.2025.11396715. Full text in Research Archive Show summary

  Natural and intuitive interaction remains a central challenge in human-robot communication. This study presents a privacy-preserving gesture recognition framework that enables command-based interaction through simple hand and arm movements. Motion data are captured using a wrist-worn sensor, eliminating the need for cameras or other intrusive tracking systems. Data from ten participants performing seven gesture classes, including move forwards, move backward, move left-right, spin horizontally, spin vertically, wave, and move up-down are used to train and validate a hybrid Convolutional Long Short-Term Memory (CNN-LSTM) model. An additional non-command state is included to ensure the robot remains inactive when no gesture is detected. The proposed model achieves an overall recognition accuracy of approximately 95% across all gesture classes using cross-validation. This framework enhances the safety, intuitiveness, and fluidity of human-robot interaction and provides a robust foundation for gesture-based robot control and human activity recognition.

• Håkon Haustreis Tønnessen, Md Zia Uddin & Jim Tørresen (2025). PINE: Planning and Identifying Neural Network for Thinking Fast and Slow. In Eisuke Kita (Eds.), 2025 11th International Conference on Robotics and Artificial Intelligence (ICRAI), December 19-21, 2025, Nagoya, Japan. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331590680. p. 191–197. doi: 10.1109/icrai68431.2025.11396706. Full text in Research Archive Show summary

  The human brain's unique ability to seamlessly switch between quick, effortless reactions and slow, deliberate thinking forms the foundation for the content of this paper. Humans effortlessly navigate complex tasks like walking, speaking, and driving, relying on heuristic responses to their environment. This natural division of labor allows humans to adapt quickly and efficiently, tackling both new challenges and familiar tasks. Drawing inspiration from this cognitive duality, we propose, in this paper, a machine-based analog that employs a two-level functionality mirroring human capabilities. The approach centers on a Reinforcement Learning agent that replicates human learning through trial and error. The thesis introduces the Planning and Identifying Neural Network (PINE), a framework that identifies tasks using a combination of inherited environmental information, observations, and human-readable task descriptors. PINE leverages a model-based Reinforcement Learning algorithm, streamlining agent complexity and significantly reducing solution-finding time compared to model-free algorithms. PINE operates in real-time, utilizing initial environmental observations and task descriptors to identify and categorize encountered tasks. For unknown tasks, PINE employs meta-learning, selecting the most similar known task to expedite the solution-finding process with fewer episodes. Despite the challenge of task identification without interaction or labels, PINE demonstrates high accuracy in recognizing previously solved tasks and providing reliable estimates for the most similar tasks. This innovative two-level learning model represents a significant step towards creating adaptive machines that efficiently handle both familiar and novel challenges.

• Letícia dos Santos, Jim Tørresen, Mariana Kolberg & Renan Maffei (2025). An Autonomous Floor Clearing Strategy to Tidy up Unknown Home Environments with a Mobile Manipulator Robot. In Emanuel Slawiñski, Cecilia García & Santiago Tosetti (Ed.), 2025 IEEE International Conference on Advanced Robotics (ICAR). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331578107. doi: 10.1109/icar65334.2025.11338715. Full text in Research Archive
• Hoang Minh Pham, Farzan Majeed Noori, Md Zia Uddin & Jim Tørresen (2025). Heart Rate Forecasting Using Ultra-Wideband Radar with Sequence-to-Sequence Model. In Luis Cruz, Sven Loncaric, Zhibo Chen, Armando J. Pinho, Jorge Batista & Marko Subasic (Ed.), Proceedings of the 14th International Symposium on Image and Signal Processing and Analysis (ISPA), Coimbra, Portugal, October 29-31, 2025. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331577551. p. 16–21. doi: 10.1109/ispa66905.2025.11259452. Full text in Research Archive Show summary

  Heart dysfunction can be very dangerous and directly associated with the risk of fatality. Therefore, automatic heart rate monitoring is important for detecting a heart abnormality early and providing timely intervention. The development of advanced sensor technology allows continuous tracking of the heart rate, as well as providing more health-related data sources, which could further improve the forecasting performance. In this study, we investigate the use of recent heart rate values and Ultra-Wideband (UWB) data in the last 20s to predict the next 10s of heart rate. Data were collected from 20 participants performing different activities. We examine the forecasting by using only a heart rate sensor (uni-modality) and by using heart rate sensor and UWB together (multi-modality). Three levels of activity were included in the study: resting (rest), lying after doing exercises (lying-abnormal) and lying after full recovery (lying-normal). A multi-modality Sequence-to-Sequence (Seq2Seq) model has a 10-percent quantile losses of 5.20 for rest activity, 4.92 for lying-abnormal activity, and 4.24 for lying-normal activity. With the lowest loss, Seq2Seq outperforms Autoregressive Integrated Moving Average (ARIMA), Error Trend and Seasonality (ETS), Transformer and Temporal Fusion Transformer (TFT) in forecasting heart rate in the three activity levels. Our results point to a new direction in multimodal prediction of heart rate to assist healthcare, using deep learning approaches.

• Shunsuke Goka, Ørjan Strand, Jun Miura & Jim Tørresen (2025). Situation-Based Navigation Strategy Switching for Mobile Robots in Dynamic Pedestrian Environments. In Takahiro Uchiya, Ryota Nishimura & Tadaaki Ikehara (Ed.), 2025 IEEE 14th Global Conference on Consumer Electronics (GCCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331524166. doi: 10.1109/gcce65946.2025.11275115. Full text in Research Archive Show summary

  This paper proposes a method for adaptively switching navigation strategies based on a visual assessment of the current situation. Despite recent progress in motion planning and navigation methods, it is still challenging to adopt a single navigation method in various situations, which are determined by both the environment structure and pedestrian distribution and movements. A promising approach is to switch between multiple navigation strategies depending on the current situation. The proposed method adopts a visual situation classification, trained on an automatically labeled dataset, for choosing an appropriate local motion planner. Experimental comparison with fixed planners using a real robot demonstrates the superiority of our method in terms of both navigation efficiency and safety.

• Shin Watanabe, Geir Horn, Jim Tørresen & Kai Olav Ellefsen (2025). Integrating Bilevel Planning and Offline Skill Learning for Enhancing Mobile Manipulation, 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). Institute of Electrical and Electronics Engineers (IEEE). ISSN 9798331522476. p. 2275–2280. doi: 10.1109/CASE58245.2025.11163881. Full text in Research Archive Show summary

  Solving complex robotic mobile manipulation tasks requires both planning the sequence of skills to execute and learning how to robustly execute each skill. Planning-based approaches such as task and motion planning (TAMP) can help train skills more efficiently through demonstrations, while learning-based approaches such as reinforcement learning (RL) can help plan tasks more quickly through heuristics. This paper presents a novel approach to generalizing mobile manipulation tasks by synergistically combining sampling-based TAMP and value-based RL. The TAMP solver first generates suboptimal demonstration trajectories of a particular skill, from which an offline RL algorithm distills a robust policy and a value function, the latter serving as a skill feasibility classifier. The policy and the classifier are both fed back into the TAMP workflow not only to improve the skill success rate but also to speed up the planner by sampling robot configurations for which the skill is likely to succeed. We evaluate the approach on a simulated block-pushing domain. Re-purposing a byproduct of an offline skill-learning process leads to an integrated planning and learning system that exploits the awareness of its own skill competence.

• Emil Engelstad Moghaddam, Md Zia Uddin, Jun Miura & Jim Tørresen (2025). A Robust Approach for Motion Skill-Based Scene Categorization. In Filippo Neri (Eds.), Proceedings of the 2025 10th International Conference on Machine Learning Technologies (ICMLT), May 23-25, 2025, Helsinki, Finland. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331536725. p. 482–487. doi: 10.1109/icmlt65785.2025.11193163. Full text in Research Archive Show summary

  This paper introduces a novel method for autonomous navigation of mobile robots, combining functional scene categorization with tailored navigation strategies. Scene categorization is a computer vision task that identifies environmental types in images. Our approach establishes a taxonomy for scene categorization with the aim of utilization in mobile robots. We used a motion-skill dataset for training different neural networks. The dataset, comprising 19923 images with nine navigation strategy labels, demonstrated the feasibility of coupling environment recognition with neural networks. Three architectures (ResNet-50, BEiT-Base, ConvNeXt-Tiny) achieved notable performance, with ConvNeXt-Tiny leading with a weighted F1 score of 0.934. Pre-training on the SUN397 dataset significantly improved the model performance, emphasizing the importance of leveraging existing scene categorization datasets. This study highlights the synergy between scene categorization and functional scene categorization, providing insights for designing efficient, context-aware autonomous navigation systems.

• Sigmund Johannes Ljosvoll Rolfsjord, Safia Fatima, Hugh Alexander von Arnim & Adel Baselizadeh (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition. In Barakova, Emilia, Allouch, Somaya Ben, Nakadai, Kazuhiro & Nejat, Goldie (Ed.), Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2025. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331587710. p. 15–20. doi: 10.1109/ro-man63969.2025.11217600. Full text in Research Archive Show summary

  IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed. The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.

• Katrine Linnea Nergård, Kai Olav Ellefsen & Jim Tørresen (2025). Fast or Slow: Adaptive Decision Making in Reinforcement Learning with Pre-Trained LLMs. In Emre Ugur, Alessandra Sciutti & Katharina Rohlfing (Ed.), 2025 IEEE International Conference on Development and Learning (ICDL). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331543433. doi: 10.1109/ICDL63968.2025.11204357. Full text in Research Archive

View all works in NVA

• Jim Tørresen (2026). Tutorial: Ethics in Embedded Systems and Robots. Full text in Research Archive
• Jim Tørresen (2026). Embedding Intelligence in Robots Through Interdisciplinary Collaboration. Full text in Research Archive
• Jim Tørresen (2026). 2nd Workshop on Robot Ethics – Ethical, Legal and User Perspectives in Robotics and Automation (WOROBET). Full text in Research Archive
• Diana Saplacan Lindblom, Sanna Kuoppamäki, Leon Bodenhagen & Jim Tørresen (2026). Presentation during Workshop at NordiCHI 2026 at the 1st Workshop on Robots as Welfare Technologies and Actors within Future Home- and Healthcare Services: Co-Designing an Integration Model through Story-Dialogue Method. Full text in Research Archive
• Diana Saplacan Lindblom (2026). Presentation at the Robophilosophy Conference: Robots as Welfare Technologies in Home and Healthcare - Some of the Lessons Learned So Far. doi: https:/www.ucd.ie/robophilosophy2026/workshops/robociety-visionandreality/. Full text in Research Archive Show summary

  he talk will present some of the lessons learned so far in some of the previous or ongoing research projects (Vulnerabilities in Robot Society-VIROS, Predictive and Intuitive Robot Companion – PIRC, and ROBOWELL- Robots as Welfare Technologies and Actors for Elderly Care). It addresses, amongst others, some of the practical and ethical challenges of integrating social robots, as actors, within care environments, seen from the user perspectives (e.g., elderly, healthcare professionals). These practical examples and ethical challenges, in their turn, will eventually lead to the more complex societal and philosophical questions about if and when, and how social robots or socially assistive robots would be appropriate to be integrated within home- and healthcare. The talk also aims to raise questions about looking at advanced (AI-based) social robots as an integrated part of a larger (healthcare or societal) system, rather than looking at these as isolated technological entities often used in single-use cases or experiments.

• Diana Saplacan Lindblom (2026). A Cross-Cultural Video-Based Study between Norway and Japan on Informed Consent and Disclosure Mechanisms on the Use of Social Robots in Public and Private Spaces - Users' Perspectives. doi: https:/ro-man2026.org/program-at-a-glance/. Full text in Research Archive
• Claudia Magdalena Sikora, Diana Saplacan Lindblom & Jim Tørresen (2026). Presentation of accepted paper: “There’s something human about it!” - Exploring users’ perception of nonverbal communication of robotic furniture. doi: https:/icsr2026.uk/programme/. Full text in Research Archive Show summary

  Non-anthropomorphic robots are integrating into our homes. People attribute human-like characteristics to these robots, although they do not resemble humans. This raises the question of whether similar processes would occur when people interact with robot furniture. Thus, the presented exploratory pilot study investigates how users intuitively interpret and perceive the non verbal communication of such robots. In an instrumental case study, we had eleven participants interacting with the T-ABLE robot through expressive behavior. Participants were interviewed about their experience. Our findings indicate that the participants use their associations between behavior and prior to similar experiences to attribute personality to the robot table. These findings provide insights for theorizing the intuitive comprehension of the behavior of non-anthropomorphic robots.

• Diana Saplacan Lindblom, Sanna Kuoppamäki, Leon Bodehagen & Jim Tørresen (2026). Webpage for 1st Workshop on Robots as Welfare Technologies and Actors within Future Home- and Healthcare Services: Co-Designing an Integration Model through Story-Dialogue Method ROBOWELL. doi: https:/sites.google.com/view/robowell-nordichi26/home?pli=1&authuser=1. Full text in Research Archive
• Diana Saplacan Lindblom, Edson Prestes, Praminda Caleb-Solly & Toshie Takahashi (2026). Diversity and International Perspectives. doi: https:/sites.google.com/view/icra2026-workshop-robot-ethics/home/programme?authuser=1. Full text in Research Archive
• Diana Saplacan Lindblom (2026). Presentation of ROBOWELL research project during MishMash launch. doi: https:/mishmash.no/events/aulaen2026/. Full text in Research Archive
• Sanna Kuoppamäki, Diana Saplacan Lindblom & Sandra Pauletto (2026). Presentation at the Welfare Technology Fair. Full text in Research Archive
• Sanna Kuoppamäki, Diana Saplacan Lindblom, Sandra Pauletto & Tien-Ying Lu (2026). Exhibition at the Welfare Technology Fair. Full text in Research Archive
• Kai Olav Ellefsen (2026). Towards more intelligent robots, that balance deep reasoning and rapid response. Full text in Research Archive Show summary

  In this talk, I first introduce broadly the research done in the group for Robotics and Intelligent Systems (ROBIN) at the University of Oslo, where we work at the intersection of robotics and artificial intelligence both in applied research and in studies aiming to better understand learning and intelligence in nature. After a broad introduction, I go more in depth on a specific research project we are currently enganged in: Predictive and Intuitive Robot Companion (PIRC). In this project, we take inspiration from human reasoning to develop AI agents that are able to balance deep reasoning with rapid response, allowing them to react quickly when needed, and to reason more carefully for cognitively demanding tasks. Dual Process Theory proposes that humans have two modes of reasoning. A fast, instinctive mode (often called System 1), which is active for instance when we quickly respond to dangers, or when we perform routine tasks; and a slow mode (often called System 2), characterized by deep reasoning and focused attention - which is active when we face new challenges or solve hard problems. Most Deep Learning systems are similar to System 1: They operate quickly, detecting patterns in inputs that are similar to what was present in their training data - a process similar to a human "instinct". In the project PIRC (Predictive and Intuitive Robot Companion) at the University of Oslo, we do research on how to perform System 2-style reasoning in robots and AI agents, and on how to implement meta-reasoning: The process of selecting between the two reasoning modes to ensure rapid, "instinctive" response when possible, and deep reasoning when required. https://www.uio.no/ritmo/english/projects/pirc/

• Kai Olav Ellefsen (2026). Hvor blir det av de virkelig intelligente robotene? doi: https:/www.mn.uio.no/ifi/om/aktuelt/arrangementer/andre/ki-dagen-2026.html. Full text in Research Archive
• Jim Tørresen (2025). Industrial AI Center Partner Presentation. Full text in Research Archive
• Jim Tørresen (2025). How should Robot Assistants behave? Full text in Research Archive
• Jim Tørresen (2025). From Adaptation of the Robot Body and Brain Using Rapid-Prototyping to Human–Robot Interaction with TIAGo. Full text in Research Archive
• Kai Olav Ellefsen (2025). Seminar: "Fast and Slow Thinking AI". Full text in Research Archive
• Alice Carnevali & Kai Olav Ellefsen (2025). Will robots do our dishes by 2030? |Euronews Tech Talks. [Internet]. EuroNews. Full text in Research Archive Show summary

  Robots are slowly making their way into agriculture, healthcare, and our homes. Should we be worried?

• Diana Saplacan Lindblom (2025). Presentation: ROBOts as Welfare Technologies and Actors for ELderLy Care: A Nordic Model for Integration of Advanced Assistive Technologies (ROBOWELL) pre-kickoff. Full text in Research Archive Show summary

  ROBOWELL - meeting with the project partners (UiO, KTH, SDU)

• Diana Saplacan Lindblom & Natalia Murashova (2025). AI in Society: Virtual and Physical AI. doi: https:/www.uio.no/om/samarbeid/skole/fagped-dag/program.html. Full text in Research Archive Show summary

  This talk explores the evolving landscape of artificial intelligence in various societal contexts, focusing on the integration and implications of AI (virtual) tools such as e.g., ChatGPT, Microsoft Copilot, but also the use of "physical AI", such as in social robots. It showcases practical cases, showing some insights from our initial fieldwork applying the Ethical Risk Assessment of AI in practice (ENACT) in various private and public (learning) organizations, as well as some insights from our work within Human-Robot Interaction and social robots area in the Vulnerability in Robot Societies (VIROS), Predictive and Intuitive Robot Companion (PIRC) research projects, and our recently funded ROBOts as Welfare Technologies and Actors for ELderLy Care: A Nordic Model for Integration of Advanced Assistive Technologies (ROBOWELL) project.

• Diana Saplacan Lindblom (2025). A Cross-Cultural Study between Norway and Japan on Consent in HRI; The Use of Social Robots in Public and Private Spaces – Users’ Perspectives. doi: https:/2025.roboethics.design/program. Full text in Research Archive
• Jim Tørresen, Edson Prestes, Praminda Caleb-Solly, Yueh Hsuan Weng & Shin Watanabe (2025). CASE 2025 Workshop on Ethical Considerations in Robotics and Automation (WECRA). doi: https:/sites.google.com/view/robot-ethics-case2025-workshop. Full text in Research Archive
• Jim Tørresen (2025). Keynote: Techno-Ethical Considerations when Applying Machine Learning in Real-world Systems. doi: https:/www.icmlt.org/2025.html. Full text in Research Archive
• Jim Tørresen (2025). Invited talk: Intelligent Robotics in Healthcare. Full text in Research Archive
• Jim Tørresen (2025). Guest lecture: Intelligent Robotics in (Home) Healthcare. Full text in Research Archive
• Jim Tørresen (2025). Invited talk: When will a robot treat us instead of a medical doctor? doi: https:/www.uio.no/english/research/strategic-research-areas/life-science/norway-life-science-conference/. Full text in Research Archive
• Diana Saplacan Lindblom (2025). Overview of my research: background, results, experiences, publications and future directions. Full text in Research Archive
• Markus Yuki Nishimura, Kohei Matsumoto & Jim Tørresen (2026). Diffusion-Based Reinforcement Learning for Multi-Robot Social Navigation. Universitetet i Oslo. Full text in Research Archive Show summary

  --- Abstract and files will be published: 2027-05-15

• Sigmund Kleivene Slåttum, Mats Erling Høvin, Benedikte Wallace & Diana Saplacan Lindblom (2026). Supervisor for Master thesis: "Moving with the Music: A Novel Robotic Floor for Motion-Enhanced Music Experience" (60 ECTS). University of Oslo (UiO). Full text in Research Archive Show summary

  This thesis investigates how synchronized whole-body movement shapes the experience of listening to music. The two research questions ask whether floor movement changes the listening experience, and whether the specific character of the movement matters for that experience. To address these questions, a one-degree-of-freedom robotic floor was designed and built from scratch. The system, named Harmonic Audio-Reactive Platform (HARP), lifts a person standing on it in a vertical rolling motion derived from features extracted from the music. Two motion patterns were implemented for use in the study: a simple pattern oscillating at the beat frequency, and a complex pattern that combines two sinusoids whose amplitudes vary with the spectral content of the music. The hardware, software, and trajectory generation are described in detail in the thesis, and form a methodological contribution alongside the empirical findings. Six participants took part in a within-group user study. Each experienced three songs from contrasting genres (classical, EDM, and metal) under three motion conditions (no motion, simple, and complex), giving nine conditions per participant. A qualitatively-driven mixed methods design was used. Semi-structured interviews were analyzed with reflexive thematic analysis, alongside Likert ratings, amplitude adjustment logs, synchronization measurements, and video observations. Three themes were constructed from the interviews: Enjoyment, Emotion, and Congruence. Across all data sources, the most consistent finding is that motion pattern preference is personal. No single pattern worked for everyone, and the same pattern could feel mismatched or stressful to one participant and meaningful to another. The movement was mostly received as adding to the listening experience, with Likert ratings, qualitative themes, and participants’ own descriptions of real-world use pointing the same way. Synchronization between music and motion shaped the experience but its specific role could not be settled within this study.

• Kevin Tran Torheim & Kai Olav Ellefsen (2025). Exploring fast and slow thinking in artificial intelligence. Universitetet i Oslo. Full text in Research Archive
• Viktor Ringvold Hasle & Kai Olav Ellefsen (2025). Fast and Slow Reasoning with Model-Based Reinforcement Learning. Universitetet i Oslo. Full text in Research Archive Show summary

  After Daniel Kahneman authored the pop-psychology book Thinking, Fast and Slow, the psychological theory of viewing human reasoning as a dual-process system has gained popularity. The dual-process theory states that human reasoning can be divided into two main modes of thinking: System 1, which is fast, intuitive, and automatic, and System 2, which is slow, deliberate, and analytical. This thesis investigates applying a dual-system approach to solving Sokoban puzzles. Sokoban is a logic puzzle game that involves moving blocks onto targets. To model these systems computationally, a learned World Model is developed to predict the consequences of future actions within the Sokoban environment. A World Model is a generative model that learns to model the dynamics of the environment. This model is used in two distinct ways: (1) to train an Actor-Critic policy that acts reflexively within the environment, representing the intuitive behavior of System 1; and (2) as a component of a planning algorithm that embodies the deliberative reasoning of System 2. A higher-level Meta-Agent is trained to dynamically arbitrate between these two modes, selecting how much planning is optimal in any state. The Meta-Agent enables a nuanced allocation of cognitive resources by dynamically adjusting the extent of System 2 processing. This novel approach represents the System 1 and System 2 dichotomy as a spectrum, rather than a binary. Sokoban puzzles are notoriously hard to solve, both computationally as well as for humans. Because of this difficulty, a high degree of exactness is required to solve them reliably. As such, one would expect the most effective reasoning strategy would be to maximize the amount of System 2 thinking. The thesis finds that this is suboptimal, and strategically planning in critical states improves the performance. The high-level Meta-Agent performs better than any pure planning approaches by minimizing the likelihood of the learned World Model generating unwanted errors during planning. This finding suggests that optimal problem-solving in complex environments benefits from a hybrid strategy that balances intuitive and analytical reasoning.

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