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About

Olov Andersson is a robotics researcher whose work spans autonomous navigation, motion planning, and real-time decision-making under uncertainty. His research bridges classical optimization-based control with modern machine learning, tackling fundamental challenges in making robots operate safely and intelligently in complex, dynamic real-world environments. Andersson's most celebrated contribution is his involvement with Team CERBERUS, whose robotic system-of-systems — integrating legged and aerial robots with resilient autonomy and mapping capabilities — won the prestigious DARPA Subterranean Challenge Finals in 2021. The associated publications have amassed over 240 citations, reflecting the significance of this landmark achievement in field robotics. His earlier work on stochastic collision avoidance using Bayesian policy optimization (44 citations) and model-based reinforcement learning in continuous environments laid important groundwork for safe autonomous navigation in populated spaces. More recently, Andersson has contributed Dynablox, a real-time mapping-based framework for detecting diverse dynamic objects, which has already attracted 67 citations since its 2023 publication. His research also addresses exploration planning, camera-only reinforcement learning navigation among humans, and collaborative rescue robotics through platforms like WARA-PS. Across these contributions, a consistent theme emerges: enabling robots to act autonomously, safely, and efficiently in environments that are messy, uncertain, and fundamentally human.