From Walking Robots to AI Errors: A Kazakh Researcher’s Path to Intelligent Systems
What should a robot do after making a mistake? The answer becomes more complicated when the person nearby does not explicitly say something has gone wrong, but merely hesitates, looks confused, or begins to lose trust in the machine. This is one of the problems currently being studied by Kazakh researcher Arman Ibrayeva. Over several years, her scientific interests have developed from mechanics and walking robots to autonomous navigation, medical robotics, and human interaction with intelligent systems. In June 2026, Ibrayeva was among the authors of REPAIR-Bench, a new research benchmark for studying how people respond to robot errors and how interaction can be restored after a failure. The researchers conducted 214 interaction trials involving 41 participants, recording speech, facial expressions, head movements, and users’ preferred responses to different robot failures. The task goes beyond simply detecting a failure. A robot working alongside people needs to understand how its mistake has changed a person’s behavior. One user may become irritated, another confused, while a third may try to repeat the action. The robot’s response should depend on that reaction. For Ibrayeva, this is a continuation of a scientific career that began not with generative AI, but with mechanics. She studied mechanics at Al-Farabi Kazakh National University, receiving her bachelor’s degree in 2018 and master’s degree in 2020. She then continued her studies at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where she conducted research in mechanical engineering. Her official KAUST profile lists robotics, mechanical engineering, and machine learning among her research interests. One of the early areas of her work was walking machines. Ibrayeva studied how their design could be simplified and the number of actuators reduced without losing the required range of movement. Such an approach can reduce energy consumption and simplify robot control. At a robotics conference at KAUST in 2022, she presented a walking robot design with decoupled limb motion. Agriculture was cited as one possible application because heavy wheeled machinery compacts soil and can damage plants. The walking mechanism was designed to move over uneven terrain while placing less load on its actuators. This work later developed into research combining classical mechanics, numerical optimization, and machine learning. A 2025 paper co-authored by Ibrayeva describes several physical prototypes of walking robots, tests on uneven surfaces, and the use of LiDAR for autonomous navigation. Another area was medical robotics. Ibrayeva took part in research on a lower-limb exoskeleton. The authors sought a design capable of reproducing the movements of the human leg with less mechanical complexity and more efficient force transmission. She also worked on autonomous navigation for service robots in rehabilitation medicine. A 2024 study examined a system using 3D LiDAR that allows a robot to map its surroundings, determine its position, and avoid obstacles while moving through a medical facility. At first glance, a walking machine for agriculture, a rehabilitation exoskeleton, and a robot interacting with a person belong to different fields. Yet all these projects involve the same practical problem: a...
