While many robots are currently deployable in factories, warehouses, and homes, their autonomous deployment requires either the deployment environments to be highly controlled, or the deployment to only entail executing one single preprogrammed task. These deployable robots do not learn to address changes and to improve performance. For uncontrolled environments and for novel tasks, current robots must seek help from highly skilled robot operators for teleoperated (not autonomous) deployment. In this talk, I will present three approaches to removing these limitations by learning to enable autonomous deployment in the context of mobile robot navigation, a common core capability for deployable robots: (1) Adaptive Planner Parameter Learning fine-tunes existing motion planners by learning from simple interactions with non-expert users before autonomous deployment and adapts to different deployment scenarios; (2) Learning Inverse Kinodynamics allows robots to learn from in-situ vehicle-terrain interactions during deployment and accurately navigate at high speeds on unstrucured off-road terrain; (3) Learning from Hallucination enables agile navigation in highly-constrained deployment environments by reflecting on previous deployment experiences and creating synthetic obstacle configurations to learn from. Building on robust autonomous navigation, I will discuss my vision toward a hardened, reliable, and resilient robot fleet which is also task-efficient and continually learns from each other and from humans. Xuesu Xiao Assistant Professor George Mason University To ask the speaker a question, click on the speech bubble icon in the lower right hand corner and type in the question in the window that pops up. The question will be sent directly to us. Please note that there is a little bit of a delay when streaming. What participants see is a few minutes behind what is happening at our end. The longer we stream, the greater the delay may become so the questions submitted at the very end may not reach us in time. The best way to get the questions answered is to send them as they come up.