Milos Prágr

My research aims to improve the autonomy of mobile robots on long-term, unsupervised missions in outdoor environments, with a current focus on terrain assessment for large, heavy duty machines.  My background is in applying onboard incremental learning and active perception to modeling traversability, a notion that describes the ease or difficulty experienced by the robot when attempting to traverse a particular terrain, and I have earned my PhD at the Czech Technical University in Prague with a dissertation on Learning Traversability from Mobile Robot Experience. 

Mobile robotics, terrain traversability assessment, onbooard learning from experience, incremental learning, mobile robot exploration.

Miloš Prágr, Jan Bayer, and Jan Faigl, On Predicting Terrain Changes Induced by Mobile Robot Traversal. In International Conference on Intelligent Robots and Systems (IROS), 2024, doi: 10.1109/IROS58592.2024.10802070.

Miloš Prágr, Jan Bayer, and Jan Faigl, Autonomous exploration with online learning of traversable yet visually rigid obstacles, Autonomus Robots, 47, 2023, doi: 10.1007/s10514-022-10075-4.

Miloš Prágr, Jan Bayer, and Jan Faigl, Autonomous robotic exploration with simultaneous environment and traversability models learning. Frontiers in Robotics and AI, 9, 2022, doi: 10.3389/frobt.2022.910113.

Miloš Prágr, Petr Čížek, Jan Bayer, and Jan Faigl, Online incremental learning of the terrain traversal cost in autonomous exploration. In Robotics: Science and Systems (RSS), 2019, doi: 10.15607/RSS.2019.XV.040.

Miloš Prágr, Petr Čížek, and Jan Faigl, Cost of transport estimation for legged robot based on terrain features inference from aerial scan. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, doi: 10.1109/IROS.2018.8593374.