Jun En Low

Vaughn College of Aeronautics and Technology, Singapore University of Technology and Design

Papers

Total Citations

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About

Jun En Low is a robotics researcher whose work pushes the boundaries of aerial manipulation, hybrid vehicle design, and real-time 3D scene representation. His research centers on enabling robots—particularly aerial platforms—to interact dynamically with their environments, whether through mid-air grasping, efficient multi-modal flight, or on-the-fly neural reconstruction. Low’s most cited work, “Aerial Grasping and the Velocity Sufficiency Region” (2022, 25 citations), introduces a novel framework for capturing airborne targets by modeling quadrotor-target interactions, defining a velocity sufficiency region that maximizes capture probability—a key step toward autonomous aerial retrieval. In “Design of a Hybrid Aerial Robot with Multi-Mode Structural Efficiency and Optimized Mid-Air Transition” (2019, 9 citations), he explores a tailless fixed-wing monocopter hybrid, achieving efficient cruising and hovering with fully utilized propulsion and aerodynamic surfaces. More recently, “NerfBridge” (2023, 5 citations), presented at the IEEE ICRA 2023 Workshop on Unconventional Spatial Representations, brings real-time, online Neural Radiance Field training to robotics, enabling expressive 3D scene modeling from color images for immediate robotic use. Low’s work consistently bridges theory and practical deployment, making him a rising figure in aerial robotics and embodied perception.