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About

Andrew Best is a versatile researcher whose work spans robotics, autonomous systems, and human-robot interaction (HRI). His most significant contribution lies in multi-agent navigation, where his 2016 algorithm for real-time reciprocal collision avoidance using elliptical agent representations — garnering 63 citations — advanced the field by enabling tighter, more realistic modeling of agents in shared environments. This foundational work has broad implications for autonomous vehicles, swarm robotics, and crowded pedestrian simulations. Beyond navigation, Best has made meaningful strides in autonomous systems safety, developing reinforcement learning-based frameworks for adversarial testing and falsification of cyberphysical systems, including self-driving cars and robots. His work on reusable adversarial agents reflects a forward-thinking approach to ensuring robustness in real-world deployment. Best's research also extends into the social dimensions of robotics. Investigating how humans perceive and respond to robotic teammates, his studies on social cues, proxemics, and HRI dynamics — including hallway navigation experiments — address the critical challenge of making robots socially intelligent collaborators rather than mere tools. His interdisciplinary range, bridging computational rigor with behavioral science, makes his work particularly relevant for researchers designing the next generation of human-centered autonomous systems.