Joseph Bruno

Papers

Total Citations

H-Index

About

Joseph Bruno’s research lies at the intersection of robotics, real-time navigation, and 3D scene representation, with a focus on enabling safe, autonomous movement in complex environments. His most impactful work, “Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps” (2025, 22 citations), introduces a novel pipeline that leverages Gaussian splatting—a cutting-edge 3D scene representation—for both planning and localization. The system comprises Splat-Plan for collision-free path generation and Splat-Loc for robust vision-based pose estimation, marking a significant step toward deploying robots in dynamic, unstructured spaces without pre-built maps. This contribution has quickly garnered attention for its practical implications in field robotics. Earlier, Bruno explored educational robotics through the “Time Constraint Finite-Horizon Path Planning Solution for Micromouse Extreme Problem” (2022, 2 citations), where he proposed an augmented competition format to teach deep learning and time-constrained planning. While less cited, this work reflects his commitment to bridging theory and hands-on learning. With his Splat-Nav pipeline already shaping real-time navigation research, Bruno is a rising voice in the robotics community, demonstrating how modern 3D representations can unlock safer, more adaptive autonomy.