Why is it so hard to deploy autonomous service mobile robots in unstructured human environments, and to keep them autonomous? In this talk, I will explain three key challenges, and our recent research in overcoming them: 1) ensuring robustness to environmental changes; 2) anticipating and overcoming failures; and 3) efficiently adapting to user needs. To remain robust to environmental changes, we build probabilistic perception models to explicitly reason about object permanence and distributions of semantically meaningful movable objects. By anticipating and accounting for changes in the environment, we are able to robustly deploy robots in challenging frequently changing environments. To anticipate and overcome failures, we introduce introspective perception to learn to predict and overcome perception errors. Introspective perception allows a robot to autonomously learn to identify causes of perception failure, how to avoid them, and how to learn context-aware noise models to overcome such failures. To adapt and correct behaviors of robots based on user preferences, or to handle unforeseen circumstances, we leverage representation learning and program synthesis. We introduce visual representation learning for preference-aware planning to identify and reason about novel terrain types from unlabelled human demonstrations. We further introduce physics-informed program synthesis to synthesize and repair programmatic action selection policies (ASPs) in a human-interpretable domain-specific language with several orders of magnitude fewer demonstrations than necessary for neural network ASPs of comparable performance. The combination of these research advances allows us to deploy a varied fleet of wheeled and legged autonomous mobile robots on the campus scale at UT Austin, performing tasks that require robust mobility both indoors and outdoors. Joydeep Biswas Assistant professor Department of Computer Science The University of Texas at Austin 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.