Lilly Kumari

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About

Lilly Kumari is a leading researcher in robotic perception, with a primary focus on advancing millimeter-wave (mmWave) radar technology. Her major contributions center on overcoming the fundamental limitations of radar—namely its poor resolution—to make it a viable, robust alternative to lidar and camera-based systems in harsh environments. Kumari’s seminal work, "High Resolution Point Clouds from mmWave Radar" (2023, 69 citations), pioneered a machine learning approach that transforms raw, low-fidelity radar data into high-resolution point clouds, enabling critical robotic functions like mapping and odometry in conditions where vision fails. She further demonstrated this breakthrough in "RadarHD: Demonstrating Lidar-like Point Clouds from mmWave Radar" (2023, 6 citations), showcasing a super-resolution pipeline that delivers lidar-quality perception through occlusions such as dust, fog, and smoke. By proving that single-chip radars can achieve rich, dense spatial data, Kumari has opened new pathways for autonomous systems operating in adverse weather and low-visibility settings. Her work is not only technically innovative but also highly practical, promising safer and more resilient robotics for real-world deployment.