Watch the Replay: Deployable Robots that Learn
Xuesu Xiao, George Mason University-
The University of Texas at Austin
Watch the replay here.
Abstract: 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 utilizes existing motion planners, fine-tunes these systems using simple interactions with non-expert users before autonomous deployment, adapts to different deployment environments, and produces robust autonomous navigation; (2) Learning Inverse Kinodynamics allows robots to learn from in-situ vehicle-terrain interactions during deployment and accurately navigate at high speeds on unstructured off-road terrain; (3) Learning from Hallucination enables agile navigation in highly-constrained deployment environments by bootstraping existing 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.
Speaker Bio: Xuesu Xiao is an incoming Assistant Professor in the Department of Computer Science at George Mason University starting Fall 2022. Currently, he is a roboticist on The Everyday Robot Project at X, The Moonshot Factory, and a research affiliate in the Department of Computer Science at The University of Texas at Austin. Dr. Xiao's research focuses on field robotics, motion planning, and machine learning. He develops highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision. Dr. Xiao received his Ph.D. in Computer Science from Texas A&M University in 2019, Master of Science in Mechanical Engineering from Carnegie Mellon University in 2015, and dual Bachelor of Engineering in Mechatronics Engineering from Tongji University and FH Aachen University of Applied Sciences in 2013.