Yi-Ling Qiao

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About

Yi-Ling Qiao is a robotics researcher whose work bridges perception, simulation, and control to advance autonomous navigation and generalist robot intelligence. Qiao’s key contributions lie in developing reliable, sensor-efficient navigation systems and differentiable physics simulation for soft-body robotics. Their most impactful work, “OF-VO: Efficient Navigation Among Pedestrians Using Commodity Sensors” (2021, 18 citations), introduces a modified velocity-obstacle algorithm that leverages probabilistic partial observations from a mono-camera and 2D Lidar to safely navigate robots through crowded pedestrian environments—a practical breakthrough for low-cost, real-world deployment. In “Differentiable Simulation of Soft Multi-body Systems” (2022, 10 citations), Qiao pioneers a top-down matrix assembly method within Projective Dynamics, enabling gradient-based optimization of soft articulated bodies with generalized dry friction, opening new avenues for robotic manipulation and biomechanics. Their forward-looking position paper, “Towards Generalist Robots: A Promising Paradigm via Generative Simulation” (2023), articulates a vision for training versatile robots through simulated environments, reflecting Qiao’s commitment to foundational, scalable AI-robotics integration. With a focus on commodity sensors and differentiable physics, Qiao’s work has shaped efficient, accessible robotic systems, earning recognition for its practical impact and theoretical rigor.