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University of Washington and NVIDIA Research
Title: Towards trusted human-centric autonomy
Abstract: Autonomous robots are becoming increasingly prevalent in everyday life, from navigating our roads and sidewalks to assisting in households and warehouses. Yet building robots that can safely and fluently interact with humans in a trusted manner remains an elusive task. Humans are remarkably adept at avoiding collisions seamlessly, even in crowded settings. In this talk, we will discuss how to utilize human interaction data to learn models that describe these interactions and explore techniques to enhance the safety and fluency of robot planning and control. First, I will discuss recent work that combines data-driven techniques with control-theoretic models to learn interpretable models of safe human-robot vehicle interactions. Second, I will discuss recent work on modeling and inferring multi-agent responsibility for avoiding collisions to gain insight into multi-agent vehicle interactions. Third, I will share some recent work on deep generative modeling for robot planning that is capable of incorporating learned interaction insights to produce safe and efficient robot behaviors.
Title: Continuous Distance Field Perception for Learning-based Control
Abstract: TBD
Title: Vision based learning in the real world
Abstract: TBD
Title: Towards Formally-Grounded Data-Driven Autonomous Driving
Abstract: TBD
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