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About

Yixing Gao is a leading researcher in assistive robotics, specializing in human-robot interaction for activities of daily living, with a particular focus on robotic dressing assistance. Her work addresses the critical challenge of personalising assistance for disabled or frail users, integrating vision and force information to enable robots to adapt to individual human poses and preferences. Gao’s most cited paper, “Iterative path optimisation for personalised dressing assistance using vision and force information” (80 citations), introduces an online method for a Baxter humanoid robot to optimise dressing paths in real time, using visual data to model human movement and force sensors to ensure safe, comfortable interaction. Her foundational work “User modelling for personalised dressing assistance by humanoid robots” (68 citations) establishes frameworks for building user-specific models, customising assistance to individual physical characteristics and behaviours. More recently, Gao has advanced robotic manipulation with “Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation” (11 citations), leveraging deep learning to recognise and handle deformable objects like clothing. Her cross-domain representation learning approach (2023) further enhances clothes unfolding, reducing reliance on synthetic data. With over 178 total citations, Gao’s research is pivotal in making assistive robots more adaptive, safe, and effective for real-world home environments, directly improving quality of life for vulnerable populations.