Arun Kumar Singh is an Associate Professor at Institute of Technology, University of Tartu. Earlier he has been a postdoc at Ben Gurion University, Israel, Nanyang Technological University, Singapore and Tampere University of Technology, Finland. His research interests lie in the algorithmic foundation of robotics in the context of motion planning and control and machine learning. The current research threads include optimization for robotics and learning, multi-agent navigation, autonomous driving, shared control, motion planning under uncertainty.

"Leveraging Predictive Uncertainty for Reliable Model-Based Planning and Control"

Model-based planning and control stands as a dominant paradigm for autonomous decision-making in robotics. In this context, the "model"—whether describing robot dynamics or environmental interactions—is increasingly learned from data. However, data-driven models are prone to errors. In this talk, I will argue for the necessity of capturing the uncertainty associated with these learned models and demonstrate how these estimates can be utilized to achieve robust planning and control. I will highlight recent advancements, including work from my own group, on learning uncertainty in a task-aware fashion. To ground these theoretical discussions, I will present applications in monocular image-based navigation and autonomous driving.