Joydeep Biswas https://www.joydeepb.com/
UT Austin
April 15, 2022

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
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 contextaware 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 preferenceaware planning to identify and reason about novel terrain types from unlabelled human demonstrations. We further introduce physicsinformed program synthesis to synthesize and repair programmatic action selection policies (ASPs) in a humaninterpretable domainspecific 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.

Learn more about Stanford's Robotics and Autonomous Systems Graduate Program and courses: https://online.stanford.edu/programs/...

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