Max Robotics — Global Service Robot Directory + US Certification

About

Yi Du is an emerging researcher at the forefront of robotic perception, simultaneous localization and mapping (SLAM), and physics-informed robot learning. His work addresses some of the most pressing challenges in autonomous systems, particularly the ability of robots to navigate reliably across diverse and unpredictable real-world conditions. Du's most notable contribution is the **SubT-MRS Dataset** (2024, 46 citations), a landmark benchmark that pushes SLAM systems toward all-weather resilience by providing rich multi-sensor data collected across challenging subterranean and outdoor environments. This dataset directly confronts a critical gap in existing SLAM research — the lack of diverse, degraded-condition data — and has quickly become a valuable resource for the robotics community. Complementing this, his involvement in **PyPose** (2023, 35 citations) reflects a broader vision for bridging deep learning and physics-based optimization in robotics. PyPose offers a principled library that helps robot learning systems generalize beyond narrow training distributions, a persistent challenge in real-world deployment. Together, these contributions highlight Du's commitment to building robust infrastructure — both datasets and software tools — that empowers the next generation of autonomous systems research. His early citation impact signals a researcher whose foundational work is already shaping the field's trajectory.