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About

Jan Weber is a robotics researcher whose work focuses on enabling autonomous mobile robots to operate safely and reliably in human-populated environments. His primary research areas include robot localization, motion planning, and human-robot interaction, with a particular emphasis on deploying robots in crowded indoor settings. Weber’s most significant contribution is the development of NDT-AMCL, a precise and robust localization algorithm that uses Normal Distribution Transforms to maintain accurate positioning even when sensors are partially obscured by moving people. This work, published in 2024, has already garnered 6 citations, with an experimental validation paper following closely. He also advanced industrial robotics by integrating reinforcement learning with inverse kinematics and motion planning for real robot manipulators, addressing practical constraints like joint angle limits—a 2021 paper that has earned 11 citations. Earlier in his career, Weber explored innovative multi-robot coordination through a spherical robot solving the classic box-pushing problem (2015). His practical contributions include a computationally efficient method for converting depth camera data into planar laser scans for collision-free 2D navigation, and simulation tools for testing robot navigation in crowds. Weber’s work bridges the gap between theoretical algorithms and real-world deployment challenges, making him a notable figure in field robotics.