Adel Baselizadeh

About

I am a researcher at the Robotics and Intelligent Systems (ROBIN) group, Department of Informatics, and the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo. My work lies at the intersection of machine learning, deep learning, reinforcement learning, multimodal sensing, control, and robotics, with applications in human–robot interaction (HRI), human activity recognition, healthcare, and music.

Selected publications

• Scientific articles and book chapters
• Lømo, Tobias; Baselizadeh, Adel; Ellefsen, Kai Olav & Tørresen, Jim (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.
• Paulsen, Geir; Cardenas, Juan Sebastian; Alsgaard, Rosa Nicoline Pham; Baselizadeh, Adel; Uddin, Md Zia & Tørresen, Jim (2025). AI-Based User Gesture Recognition for Human-Robot Interaction Using Wrist Sensors. In Kita, Eisuke (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.
• Baselizadeh, Adel; Uddin, Md Zia; Khaksar, Weria; Lindblom, Diana Saplacan & Tørresen, Jim (2025). Privacy-Preserving 3D Lidar-Based Multi-Modal Activity Recognition in Human-Robot Interaction. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. 2025, p. 516–523. doi: 10.1109/iccma67641.2025.11369549. Full text in Research Archive Show summary Human activity recognition (HAR) involves using sensors to collect human data, which is then analyzed to identify their activities. HAR has numerous applications across various fields. One significant area is human-robot interaction (HRI). Recognizing user activities provides robots with valuable insights into the user’s status, enhancing the efficiency of HRI. However, developing HAR models presents privacy challenges due to the collection of user data, especially in robots, which are often equipped with diverse sensors. This paper proposes privacy-preserving HAR methods within the context of HRI.The paper investigates using privacy-preserving sensors, with a particular focus on 3D Lidar, to develop HAR models. It explores the integration of 3D Lidar with other sensors, including user-wearable sensors and robot-based sensors, including force/torque sensors, through multimodal deep learning (DL) approaches. Various DL-based sensor fusion methods, including data-level and feature-level fusion approaches, are thoroughly examined, and their accuracies for HAR are compared.A novel dataset was collected to train the multimodal DL models, capturing various user activities during HRI. This dataset leverages 10 different sensors, including 9 privacy-preserving sensors, along with an RGB camera for reference. The paper considers nine distinct user activities, including physical interactions with a robot and commanding a robot to perform specific tasks. The results indicate that integrating the sensors’ data at the feature-level achieves an 80.73% accuracy in recognizing various user activities during HRI.
• 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). 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.
• Meijer, Frida; Lindblom, Diana Saplacan; Baselizadeh, Adel & Tørresen, Jim (2025). "The wooden gripper was warmer and made the robot less threatening"– 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). ISSN 9798331587710. p. 1091–1098. doi: https:/ieeexplore.ieee.org/document/11217775. Full text in Research Archive 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 – 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.
• Maeda, Ryuichi; Baselizadeh, Adel; Watanabe, Shin; Kurazume, Ryo & Tørresen, Jim (2025). Adaptive Tidying Robots: Learning from Interaction and Observation. In Asfour, Tamim; Ramírez-Amaro, Karinne; Kim, Joohyung & Cheng, Gordon (Ed.), 2025 IEEE/SICE International Symposium on System Integration (SII). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331531614. p. 185–192. doi: 10.1109/sii59315.2025.10871003. Full text in Research Archive
• Otterdijk, Marieke van; Laeng, Bruno; Lindblom, Diana Saplacan; Baselizadeh, Adel & Tørresen, 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. 17, p. 2279–2292. doi: 10.1007/s12369-025-01271-0. Full text in Research Archive Show summary 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.
• Orten, Kristine Fjellkårstad; Helgesen, Sander Elias Magnussen; Chen, Bihui; Baselizadeh, Adel; Tørresen, Jim & Herrebrøden, Henrik (2025). Can machine learning distinguish between elite and non-elite rowers? International Journal of Computer Science in Sport. 24(1), p. 118–132. doi: 10.2478/ijcss-2025-0007. Full text in Research Archive Show summary A major challenge for sports coaches and analysts is to identify critical elements of athletes’ movement patterns. A potentially relevant tool is machine learning, useful because of its ability to extract patterns from data. In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. The MLP model achieved an accuracy of 100% when using all input features, indicating that the problem is suitable as a machine learning task. Our research focused on using a limited amount of the data. Despite using fewer input features, the models managed to classify skill levels with reasonable precision, reaching a best performance of 77% accuracy for the model combining GRU and CNN architectures, 78% for the GRU model, and 94% for the MLP model. From a rowing perspective, the results suggest that movement coordination between upper and lower body limbs, as represented by different feature combinations, is informative in distinguishing between elites and non-elites. The current work suggests that machine learning may supplement human experts in sports coaching, analytics, and talent identification.