PIRC Predictive and Intuitive Robot Companion

• Moghaddam, Emil Engelstad; Uddin, Md. Zia; Miura, Jun & Torresen, Jim (2025). A Robust Approach for Motion Skill-Based Scene Categorization. In Neri, Filippo (Eds.), Proceedings of the 2025 10th International Conference on Machine Learning Technologies (ICMLT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331536725. doi: 10.1109/icmlt65785.2025.11193163. 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.

• Rolfsjord, Sigmund Johannes Ljosvoll; Fatima, Safia; Arnim, Hugh Alexander von & Baselizadeh, Adel (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition. In Emilia, Barakova,; Ben, Allouch, Somaya; Kazuhiro, Nakadai, & Goldie, Nejat, (Ed.), Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2025. IEEE (Institute of Electrical and Electronics Engineers). p. 15¨C20. doi: 10.1109/ro-man63969.2025.11217600. 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.

• Nerg?rd, Katrine Linnea; Ellefsen, Kai Olav & T?rresen, Jim (2025). Fast or Slow: Adaptive Decision Making in Reinforcement Learning with Pre-Trained LLMs. In Ugur, Emre; Sciutti, Alessandra & Rohlfing, Katharina (Ed.), 2025 IEEE International Conference on Development and Learning (ICDL). IEEE (Institute of Electrical and Electronics Engineers). doi: 10.1109/ICDL63968.2025.11204357.
• Meijer, Frida; Lindblom, Diana Saplacan; Baselizadeh, Adel & T?rresen, Jim (2025). "The wooden gripper was warmer and made the robot less threatening"¨C A Study on Perceived Safety based on Robot Gripper¡¯s Visual and Tactile Properties. In Emilia, Barakova,; Ben, Allouch, Somaya; Kazuhiro, Nakadai, & Goldie, Nejat, (Ed.), Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2025. IEEE (Institute of Electrical and Electronics Engineers). p. 1091¨C1098. doi: https:/ieeexplore.ieee.org/document/11217775. Show summary

  An ageing population and the need of providing adequate care have led to developing robots to relieve healthcare workers and to assist individuals in their own homes. However, the successful integration of robots in such settings relies on more than just ensuring physical safety associated with physical risks (e.g., collisions): it also requires the user¡¯s perceived safety ¨C the users perceiving the robot as not doing any harm. This paper explores the potential influence of a robot grippers¡¯ visual and tactile properties, such as materials and texture, on the users¡¯ perceived safety and comfort of human-robot interaction. An initial survey was distributed to 53 participants, exploring five (n=5) robot gripper designs focusing on the robots¡¯ gripper shape. One design shape was thereafter selected to be constructed as a cover to be placed over the parallel grippers of the TIAGo robot, by using 1) wood filament and 2) plastic. The covers were then tested in an experimental setting with 11 participants. The covers were attached to the TIAGo mobile manipulator robot and participants interacted with both of the designed gripper covers within a controlled laboratory environment. A questionnaire was distributed to all 11 experiment participants, at different stages of the interactions. The findings indicate that the material of the gripper influenced participants¡¯ sense of comfort, familiarity, and perceived capabilities of the robot. The study suggests that perceived safety in human-robot interaction (HRI) is shaped not only by physical factors but also by how materials are personally and contextually interpreted. To better support safe and comfortable interactions, further research is needed to understand how material choices shape users¡¯ perceived safety.

• Otterdijk, Marieke van; Neggers, Margot; Torresen, Jim & Barakova, Emilia (2025). Exploring Human Attribution of?Emotional Intent to?Motion Features in?a?Humanoid Robot. In Palinko, Oskar; Bodenhagen, Leon; Cabibihan, John-John; Fischer, Kerstin; ?abanovi?, Selma; Winkle, Katie; Behera, Laxmidhar; Ge, Shuzhi Sam; Chrysostomou, Dimitrios; Jiang, Wanyue & He, Hongsheng (Ed.), Social Robotics. Springer. ISSN 9789819635184. p. 323¨C323. doi: 10.1007/978-981-96-3519-1_29.
• Maeda, Ryuichi; Baselizadeh, Adel; Watanabe, Shin; Kurazume, Ryo & T?rresen, Jim (2025). Adaptive Tidying Robots: Learning from Interaction and Observation. In Asfour, Tamim; Ram¨ªrez-Amaro, Karinne; Kim, Joohyung & Cheng, Gordon (Ed.), 2025 IEEE/SICE International Symposium on System Integration (SII). IEEE (Institute of Electrical and Electronics Engineers). p. 185¨C192. doi: 10.1109/sii59315.2025.10871003.
• Otterdijk, Marieke van; Laeng, Bruno; Saplacan-Lindblom, Diana; Baselizadeh, Adel & T?rresen, Jim (2025). Seeing Meaning: How Congruent Robot Speech and Gestures Impact Human Intuitive Understanding of Robot Intentions. International Journal of Social Robotics. ISSN 1875-4791. doi: 10.1007/s12369-025-01271-0. Show summary

  Abstract Social communication between humans and robots has become critical as a result of the integration of robots into our daily lives as assistants. There is a need to explore how users intuitively understand the behavior of a robot and the impact of social context on that understanding. This study measures mental effort (as indexed by pupil response) and processing time, measured as the time taken to provide the correct answer, to investigate participants¡¯ intuitive understanding of the robot¡¯s gestures. Thirty-two participants participated in a charades game with a TIAGo robot, during which their eyes were tracked. Our findings show a relationship between mental effort and processing time, and indicate that robot gestures, congruence of speech and behavior, and the correctness of interpreting robot behavior influence intuitive understanding. Furthermore, we found that people focused on the robot¡¯s limb movement. Using these findings, we can highlight what features contribute to the intuitive interaction with a robot, thus improving its efficiency.

• Weng, Yueh-Hsuan; Torabi, David; Torresen, Jim; Dong, Zonghao & Hirata, Yasuhisa (2025). Bridging Ethics and Reality: Integrating Thought Experiments and Empirical Insights in Robot Ethics. IEEE robotics & automation magazine. ISSN 1070-9932. p. 8¨C8. doi: 10.1109/mra.2025.3584352.
• Fatima, Safia; Ellefsen, Kai Olav & Moonen, Leon (2025). Self Healing of a Mixed Autonomy Traffic System Using Reinforcement Learning and Attention. IEEE Open Journal of Intelligent Transportation Systems. 6, p. 1200¨C1220. doi: 10.1109/OJITS.2025.3606539. Show summary

  As urban traffic becomes increasingly complex with the integration of connected and autonomous vehicles alongside human-driven vehicles, there is a critical need for adaptive traffic management systems capable of self-healing in response to disruptions. This paper introduces TS2RLA (¡°Traffic System Recovery using Reinforcement Learning and Attention¡±), a novel framework for self-healing in mixed-autonomy traffic systems by combining deep reinforcement learning with an attention mechanism to optimize traffic flow and recover from faults in various scenarios in a mixed-autonomy traffic environment. We evaluated TS2RLA in four complex traffic scenarios: bottleneck, figure-eight, grid, and merge. Our results demonstrate significant improvements over the baseline model, showing an average of 86.74% reduction in crashes, 71% improvement in speed and traffic throughput, and robust performance under diverse and complex traffic conditions. Moreover, our experiments show that TS2RLA leads to a significant reduction in CO2 emissions and fuel consumption. TS2RLA¡¯s attention-based approach shows particular benefits in bottleneck and figure-eight scenarios, demonstrating its ability to adapt to complex, multi-factor traffic situations. For scenarios that TS2RLA had not been trained on before, it performs even more favorably than the baseline, with a 96.8% crash reduction and 95.3% throughput improvement. This shows its ability to adapt effectively to new traffic conditions. Overall, we conclude that TS2RLA could significantly improve the safety, efficiency, and capacity of real-world traffic systems, particularly in dynamic urban environments. As such, our work contributes to the field of intelligent transportation systems by offering a versatile self-healing framework capable of managing the complexities of mixed-autonomy traffic.

• Orten, Kristine Fjellk?rstad; Helgesen, Sander Elias Magnussen; Chen, Bihui; Baselizadeh, Adel; T?rresen, Jim & Herrebr?den, Henrik (2025). Can machine learning distinguish between elite and non-elite rowers? International Journal of Computer Science in Sport. 24(1), p. 118¨C132. doi: 10.2478/ijcss-2025-0007.
• Khaksar, Weria; Saplacan, Diana; Bygrave, Lee Andrew & T?rresen, Jim (2025). Robotics in Elderly Healthcare: A Qualitative Analysis of 20 Recent European Research Projects. ACM Transactions on Human-Robot Interaction. 14(2). doi: 10.1145/3711936.
• Otterdijk, Marieke van; Kwak, Dongho; Baselizadeh, Adel; Lindblom, Diana Saplacan & Torresen, Jim (2024). Age-Old Gesture: Analyzing the Intuitive Responses to Robot Handshakes Among Seniors and Young Adults. In Sgorbissa, Antonio & Wei, Lei (Ed.), 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350375022. doi: 10.1109/ro-man60168.2024.10731393.
• Baselizadeh, Adel; Lindblom, Diana Saplacan; Khaksar, Weria; Uddin, Md Zia & T?rresen, Jim (2024). Comparative Analysis of Vision-Based Sensors for Human Monitoring in Care Robots: Exploring the Utility-Privacy Trade-off. In Sgorbissa, Antonio & Wei, Lei (Ed.), 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350375022. p. 1794¨C1801. doi: 10.1109/ro-man60168.2024.10731223.
• Watanabe, Shin; Horn, Geir; T?rresen, Jim & Ellefsen, Kai Olav (2024). Improving Robot Skills by Integrating Task and Motion Planning with Learning from Demonstration. In Capi, Genci (Eds.), 2024 6th International Conference on Control and Robotics. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331518158. doi: 10.1109/iccr64365.2024.10927043.
• Baselizadeh, Adel; Uddin, Md Zia; Khaksar, Weria; Saplacan, Diana & T?rresen, Jim (2024). PriMA-Care: Privacy-Preserving Multi-modal Dataset for Human Activity Recognition in Care Robots. In Grollman, Dan & Broadbent, Elizabeth (Ed.), HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. Association for Computing Machinery (ACM). ISSN 9798400703232. p. 233¨C237. doi: 10.1145/3610978.3640701. Full text in Research Archive
• Otterdijk, Maria Theodorus Henricus van; Neggers, Margot; T?rresen, Jim & Barakova, Emilia (2024). A Study on Congruency in Expressive Robot Movement While Delivering Food to Seniors. In Zhang, Houxiang & Shi, Qing (Ed.), Proceedings of the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350372601. p. 431¨C436. doi: 10.1109/RCAR61438.2024.10670931.

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

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

• Lindblom, Diana Saplacan & Murashova, Natalia (2025). AI in Society: Virtual and Physical AI. doi: https:/www.uio.no/om/°ÄÃŻʹÚÌåÓý,»Ê¹Ú×ãÇò±È·Ö/skole/fagped-dag/program.html. 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.

• Lindblom, Diana Saplacan (2025). A Cross-Cultural Study between Norway and Japan on Consent in HRI; The Use of Social Robots in Public and Private Spaces ¨C Users¡¯ Perspectives. doi: https:/2025.roboethics.design/program.
• T?rresen, Jim; Prestes, Edson; Caleb-Solly, Praminda; Weng, Yueh Hsuan & Watanabe, Shin (2025). CASE 2025 Workshop on Ethical Considerations in Robotics and Automation (WECRA). doi: https:/sites.google.com/view/robot-ethics-case2025-workshop.
• T?rresen, Jim (2025). Keynote: Techno-Ethical Considerations when Applying Machine Learning in Real-world Systems. doi: https:/www.icmlt.org/2025.html.
• T?rresen, Jim (2025). Invited talk: Intelligent Robotics in Healthcare.
• T?rresen, Jim (2025). Guest lecture: Intelligent Robotics in (Home) Healthcare.
• T?rresen, Jim (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/.
• Lindblom, Diana Saplacan (2025). Overview of my research: background, results, experiences, publications and future directions.
• Lindblom, Diana Saplacan (2025). "The Wooden Gripper Was Warmer and Made the Robot Less Threatening"¨C a Study on Perceived Safety Based on Robot Gripper¡¯s Visual and Tactile Properties.
• Rolfsjord, Sigmund Johannes Ljosvoll; Arnim, Hugh Alexander von; Fatima, Safia & Baselizadeh, Adel (2025). Multimodal Transfer Learning for Privacy in Human Activity Recognition.
• Nerg?rd, Katrine Linnea; Ellefsen, Kai Olav & T?rresen, Jim (2025). Fast or Slow: Adaptive Decision Maiking in Reinfocement Learning with Pre-Trained LLMs.
• Ellefsen, Kai Olav (2025). Fast and Slow Thinking AI.
• Saplacan, Diana (2025). Overview of the Robotics and Intelligent Research Group work within Human-Robot Interaction from the past years.
• Saplacan, Diana (2025). Overview of the Work Within HRI Focusing on Elderly Care and Healthcare Professionals Views on the Use of Robots within Home- and Healthcare: Lessons Learned.
• Tapus, Adriana; Zhegong, Shangguan; T?rresen, Jim & S?raa, Roger Andre (2024). IEEE RO-MAN 2024 Workshop on Ethics Challenges in Socially Assistive Robots and Agents: Legality, Value Orientation, and Future Design for Human-Robot Interaction (HRI). doi: https:/perso.ensta-paris.fr/~shangguan/Ro-manWS/Home.html.
• T?rresen, Jim; Prestes, Edson; Caleb-Solly, Praminda & Weng, Yueh Hsuan (2024). IEEE/RSJ IROS 2024 workshop Ethical, Legal and User Perspectives on Assisting Robots and Systems (WELUPARS). doi: https:/sites.google.com/view/iros-2024-robot-ethics?pli=1&authuser=1.
• T?rresen, Jim (2024). Invited talk: Intelligent robots - the future of healthcare?
• T?rresen, Jim (2024). Invited talk: Ethics Integrated in Human-Robot Interaction Research. doi: https:/www.roboethics.design.
• Lindblom, Diana Saplacan (2024). Healthcare Professionals¡¯ Attitudes Towards Caregiving Through Teleoperation of Robots in Elderly Care. Seminar at RITMO Centre of Excellence for Time, Rhythm, and Motion. Show summary

  This week's Food and Paper will be given by Diana Saplacan.

• Ellefsen, Kai Olav (2024). For KI er to pluss to like tidkrevende som ? l?se klimakrisen. Morgenbladet. ISSN 0805-3847.
• Saplacan, Diana (2024). User Studies in HRI: A Qualitative Research Perspective.
• Saplacan, Diana (2024). Social Robots and Socially Assistive Robots (SARs) within Elderly Care: Lessons Learned So Far.
• Saplacan, Diana (2024). Human Robot Interaction: Studies with Users.
• Saplacan, Diana (2024). Qualitative Observational Video-Based Study on Perceived Privacy in Social Robots¡¯ Based on Robots Appearances.
• T?rresen, Jim (2024). Ethical, Legal and Technical Challenges and Considerations.
• T?rresen, Jim (2024). Invitert foredrag: Vil vi ha roboter til ? hjelpe oss n?r vi trenger hjelp?
• T?rresen, Jim (2024). Kunstig intelligens og forskningsetiske vurderinger.
• Torheim, Kevin Tran & Ellefsen, Kai Olav (2025). Exploring fast and slow thinking in artificial intelligence. Universitetet i Oslo. Show summary

  Artificial intelligence systems have shown great success in several fields of study, but they still lack human abilities like adaptability and generalization. In 2011, psychologist Daniel Kahneman suggested that the human mind is composed of two different thinking systems, System 1 (fast thinking) and System 2 (slow thinking), in his book ¡°Thinking, Fast and Slow¡±. System 1 is fast and effortless, and is often used to solve simple tasks. System 2 is slow and effortful, and is used to solve complex tasks that require reasoning. Kahneman¡¯s theory has inspired artificial intelligence researchers to implement artificial intelligence agents that are composed of fast and slow thinking algorithms, which resemble System 1 and System 2 in humans. In reinforcement learning, this hybrid agent is called a meta-agent. It is worth exploring meta agents since they can be more adaptable to their environments. This thesis further explores the meta agent based on previous research in a new environment called Pushworld, which is a complex puzzle game that requires a lot of planning. Previous research has mostly focused on training a meta agent to achieve the highest reward in different environments, without considering that the meta agent can be prone to overusing a thinking system. The main objective of this thesis is therefore to implement and train a meta agent that is more adaptable to its environment by switching between fast and slow thinking autonomously, without overusing a thinking system in the Pushworld environment. To simulate that slow thinking is effortful and prevent the meta agent from overusing slow thinking, the meta agent receives a slow thinking penalty for using slow thinking. The thesis gives an analysis of how prone the meta agent is to overusing fast or slow thinking when given different amounts of slow thinking penalties. This thesis also explores to what extent slow thinking is advantageous for decision making, and to what degree the meta agent can benefit from having fast and slow thinking in Pushworld. The results show that the penalty affects the meta agent¡¯s performance in solving puzzles and how much fast and slow thinking the meta agent uses. The meta agent uses more fast thinking when the penalty increases, and achieves less performance in solving Pushworld puzzles. The meta agent prefers to use more slow thinking when the penalty decreases and performs better in solving puzzles. The key message of this thesis is that the meta agent benefits from having fast and slow thinking when the slow thinking penalty is balanced. The findings are important because they contribute to implementing meta-agents that can balance the usage of fast and slow thinking.

• Hasle, Viktor Ringvold & Ellefsen, Kai Olav (2025). Fast and Slow Reasoning with Model-Based Reinforcement Learning. Universitetet i Oslo. 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.

• Bjerkan, Mons Eirik; Watanabe, Shin & T?rresen, Jim (2025). Offline Reinforcement Learning through MPC Bootstrapping. Universitetet i Oslo. doi: http:/hdl.handle.net/10852/120967.
• Sandanger, Tonje Viddal; T?rresen, Jim; Wiig, Ola; Homlong, Eirik Gromholt & Kumar, Rahul Prasanna (2025). Enhancing Gait Analysis through eXplainable AI. Universitetet i Oslo. doi: http:/hdl.handle.net/10852/120947.
• Lindblom, Diana Saplacan; T?rresen, 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.
• Chen, BiHui; T?rresen, Jim & Sanfilippo, Filippo (2024). Simulation-to-real-world reinforcement learning with the PAL TIAGo robot. Universitetet i Oslo.

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