Lingyi Huang

Rutgers Sexual and Reproductive Health and Rights, Rutgers, The State University of New Jersey

Papers

Total Citations

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About

Lingyi Huang is a robotics researcher whose work focuses on the intersection of motion planning, neural networks, and hardware acceleration. Her primary research areas include robot motion planning, spatio-temporal neural networks, and efficient computing architectures for autonomous systems. Huang's major contribution lies in developing novel neural network-based motion planners that treat motion planning as a video prediction problem, enabling more efficient and parallelizable trajectory computation. Her most cited work, "Robot Motion Planning as Video Prediction" (2022, 15 citations), introduces a spatio-temporal neural network approach that leverages the learning capabilities of NN models for high-quality, collision-free path generation. She further advanced the field with "MOPED" (2024, 4 citations), a flexible motion planning engine designed to handle varying dimensional spaces while reducing computational overhead. Additionally, her invited paper on hardware architecture for graph neural network-enabled motion planners (2022, 2 citations) bridges the gap between algorithmic innovation and practical deployment, addressing the critical need for efficient hardware implementation in real-world robotic applications. Huang's research is particularly notable for its holistic approach, combining algorithmic design with hardware considerations to push the boundaries of autonomous navigation in 2D and 3D environments.