Abstract: Decision-making in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning, where decision-making is decomposed into reasoning[…]
Abstract: This LCSR Professional Development Seminar is focused on essential skills for interviewing for technical jobs in industry and in academia BIO: Marin Kobilarov is an Associate Professor at the Johns Hopkins University and a[…]
Title: Robot Application Development: A Shifting Paradigm Abstract: Interfaces for Robot Application Development (RAD) have proven effective at empowering non-roboticist developers (i.e., robot end users and non-robotics domain experts) to specify tasks for robots to[…]
Abstract: Robots must behave safely and reliably if we are to confidently deploy them in the real world around humans. To complete tasks, robots must manage a complex, interconnected autonomy stack of perception, planning, and[…]
Model-Based Methods in Today’s Data-Driven Robotics Landscape Seth Hutchinson, Georgia Tech Abstract: Data-driven machine learning methods are making advances in many long-standing problems in robotics, including grasping, legged locomotion, perception, and more. There are, however,[…]
Title: “Assured robotic super-autonomy” Abstract: In recent years, we have observed the rise of robotic super-autonomy, where computational control and machine learning methods enable agile robots to far exceed the performance of conventional autonomous systems. However, as[…]
Abstract: Humans can effortlessly construct rich mental representations of the 3D world from sparse input, such as a single image. This is a core aspect of intelligence that helps us understand and interact with our[…]