MRC Seminar: Deploying Autonomous Service Mobile Robots, And Keeping Them Autonomous
Friday, October 7, 2022
Deploying Autonomous Service Mobile Robots, And Keeping Them Autonomous
Department of Computer Science
The University of Texas at Austin
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 is an assistant professor in the department of computer science at the University of Texas at Austin. He earned his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008, and M.S. and PhD in Robotics from Carnegie Mellon University in 2010 and 2014 respectively. From 2015 to 2019, he was assistant professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research spans perception and planning for long-term autonomy, with the ultimate goal of having service mobile robots deployed in human environments for years at a time, without the need for expert corrections or supervision. Prof. Biswas received the NSF CAREER award in 2021, an Amazon Research Award in 2018, and a JP Morgan Faculty Research Award in 2018.
Host: Ryan Sochol