01. School of Engineering and Digital Sciences

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    ADAPTING TO LEARNER’S COGNITIVE DIFFERENCES USING REINFORCEMENT LEARNING
    (Nazarbayev University School of Engineering and Digital Sciences, 2023-05-02) Nurgazy, Symbat; Issa, Ilyas; Kassymbekov, Saparkhan; Kuangaliyev, Zholaman
    This report explores the benefits and challenges of creating adaptive robotic systems by integrating open-source software and reinforcement learning algorithms. The aim is to develop robotic systems that adapt to cognitive differences of the child to increase the engagement time and develop social skills using robot-assisted therapy. The Furhat and NAO robots are used as the platforms for receiving inputs and outputs, while a reinforcement learning agent selects the order of ac- tivities. The state space includes the pose landmarks from the Mediapipe and OpenPose libraries to recognize the engagement level and some static parameters such as age, autism level, verbality, and the co-diagnosis of Attention Deficit Hyperactivity Disorder. The actions space contains the act of changing the current activity. In the current stage, we have eight types of activities the robot choose to interact with the child. Furhat robot is programmed to have a full pipeline for a conversational robot: automatic speech recognition, text-to-speech, machine translation, and OpenAI language model. The results of this project provide insight into the potential of using open-source software and reinforcement learning algorithms to create more advanced interactive robots and highlight the importance of continued research in the field of Human-Robot Interaction.
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    COMPUTATIONAL FLUID DYNAMICS IN PANCREATICOBILIARY JUNCTION
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04) Turgaliyev, Alim
    Pancreaticobiliary maljunction without biliary dilation is associated with pancreaticobiliary reflux, a pathophysiologic factor underlying a wide range of diseases, including gallbladder cancer. This work used computational flow simulations to examine the mechanisms and relevant geometric features that influence pancreaticobiliary reflux and assess the impact of surgical interventions. The results suggest that the refilling phase is the primary mechanism driving pancreaticobiliary reflux. Moreover, the cystic duct diameter was the most critical factor determining the reflux dynamics. Furthermore, the configuration of the baffle system (the baffle height ratio and the number of baffles) affected the dynamics of pancreaticobiliary reflux. Also, the study underscores the potential therapeutic efficacy of cholecystectomy and endoscopic retrograde cholangiopancreatography in managing pancreaticobiliary reflux in cases of pancreaticobiliary maljunction without biliary dilatation. These interventions offer promising avenues for reducing reflux-related complications and mitigating the progression of associated diseases.
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    DEVELOPMENT OF BRAIN-BASED SMART-HOME/TYPING SYSTEM
    (Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Yergaliyeva, Aiana; Berikbol, Arnur; Seiilkhan, Arsen
    Our project combines EEG-EOG signals to develop an efficient Brain-Computer Interface (BCI) spelling system for Virtual Reality (VR) and Mixed Reality (MR) environments. This hybrid speller enables users to spell using brain activity by leveraging multi-modal signals and various classification strategies. Aimed at improving the quality of life for individuals with motor disabilities, such as spinal cord injuries, ALS, locked-in syndrome, and the elderly, our BCI system provides an alternative communication channel. Focusing on the well-established P300 Row-Column (RC) speller paradigm, we incorporate convolutional neural network (CNN) classification techniques for enhanced performance. Additionally, we use mixed reality glasses to improve user comfort and EEG signal quality. Our methodology includes comprehensive experimental procedures, from environment setup to data analysis and iterative refinement. By advancing BCI technology and integrating VR and MR interfaces, our project seeks to promote accessibility and inclusivity, enabling individuals of all abilities to communicate and participate more fully in social, educational, and professional activities.
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    PERCEPTION OF SAFETY AND ROBOT INTELLIGENCE IN PHYSICAL HUMAN-ROBOT INTERACTION
    (Nazarbayev University School of Engineering and Digital Sciences, 2023-12-06) Tusseyeva, Inara
    This thesis focuses on the study of two important aspects of physical human-robot interaction (pHRI): perceived safety– the subjective feeling of safety of a human operator in the physical presence of a robot– and perceived robot intelligence. The first part of the thesis focuses on reviewing published papers with the goal of understanding what factors influence these two aspects. It was found that, in general, factors influencing the perception of safety are human-robot distance, robot speed, direction of approach, robot size and appearance, motion fluency and predictability, communication and smooth contacts. On the other hand, factors influencing the perceived intelligence of a robot are transparency, animacy, trust, human-like appearance and gestures; other aspects such as adaptability are also influencing perceived intelligence not only for robots, but for intelligent agents in general. Habituation also seems to influence perceived intelligence in some cases, causing it to increase. The second part of the thesis is related to experiments to study the influence of the above-mentioned factors. Experiments were run in which a human subject shared the workspace with a collaborative manipulator while carrying out independent tasks. Different algorithms were used to plan the motion of the robot, all of which applied the safety standard known as speed and separation monitoring (SSM), i.e., the robot speed was decreased proportionally to the distance with the human to guarantee that the robot could stop before a collision occurred. One algorithm generated a fixed path (FP) of the robot with SSM-based modulated speed. A second algorithm (based on model predictive control, or MPC in short), kept updating the robot motion based on the current human location, aimed at increasing productivity compared to the FP case. Two variants of these algorithms (namely, FP-HR and MPC-HR) were specifically developed in this thesis, to further decrease the robot speed based on heart rate measurements. The analysis of the experimental results obtained for 48 subjects showed that MPC was perceived as less safe than FP, which in turn was perceived as safer than FP-HR. The first result was expected, as the MPC-generated motion is in general less predictable, while the second was unexpected, and probably due to frequent pauses of the robot in the FP-HR case. In general, it was observed that further reducing robot speed based on heart-rate measurements did not improve perceived safety; this can be explained by the presence of a lightweight collaborative robot and by the application of SSM, factors that made the non-HR variants of the algorithms already perceived as sufficiently safe, so that no improvement was noticed when introducing the HR-based variants of the same algorithms. Also, participants did not find the robot more intelligent when its motion was governed by more complex algorithms; this can be explained by a relative lack of transparency. In other words, participants had no actual insight in the motion planning algorithms, and did not manage to fully understand their differences during experiments; this caused all algorithms to be perceived as equally intelligent. Apart from differences between single algorithms, it was found that habituation improved both perceived safety and perceived intelligence– indeed, each participant interacted with the robot in four subsequent sub-sessions– and that the previous experience of the participants in interacting with robots played no role in their perception of safety and robot intelligence.