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About

Yuting Guo is a rising researcher in the fields of bionic prosthetics, human-robot interaction, and adaptive control. Her work focuses on developing intelligent control systems that enable prosthetic limbs and robots to interact with their environments more naturally and autonomously. One of her key contributions is a bionic hand motion control method that combines imitation learning of human hand movements with reinforcement learning, achieving a more intuitive and adaptive control strategy (11 citations, 2024). She has also advanced shoulder-disarticulation prosthetics by designing an interactive control algorithm that uses reinforcement learning to compensate for the loss of limb function in amputees (4 citations, 2024). More recently, Guo proposed a force observer-based motion adaptation and adaptive neural control system for robots operating in unknown environments, eliminating the need for physical sensing devices while enabling safe and effective contact with surroundings (3 citations, 2025). Her work is notable for its inspiration from human interaction strategies, bridging the gap between robotic systems and real-world, unstructured environments. With a growing citation record and a focus on practical, human-centered robotics, Yuting Guo is establishing herself as an innovator in assistive and interactive robotic technologies.