Deployable Robots that Learn

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) Interactive Learning by 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) In-Situ Learning of vehicle kinodynamics allows robots to learn from vehicle-terrain interactions during deployment and accurately, quickly, and stably navigate robots on unstructured off-road terrain; (3) Reflective Learning by 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. In addition, I will also briefly introduce a few more recent work on robot night vision, social robot navigation, multi-robot coordination, and human-robot interaction.

Biography

Xuesu Xiao is an Assistant Professor in the Department of Computer Science at George Mason University. Xuesu (Prof. XX) directs the RobotiXX lab, in which researchers (XX-Men and XX-Women) and robots (XX-Bots) work together at the intersection of motion planning and machine learning with a specific focus on developing highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision. Xuesu’s work has been deployed in real-world robot field missions, including search and rescue effort in the Mexico City earthquake and the Greece refugee crisis, decommissioning effort in the Fukushima nuclear disaster, and multiple search and rescue exercises in the US. Xuesu’s research has been featured by The New York Times, WIRED, US Army, and IEEE Spectrum. Xuesu has been awarded State Council of Higher Education for Virginia (SCHEV) Outstanding Faculty Award (OFA) Rising Star, the Commonwealth’s highest honor for faculty at Virginia’s public and private colleges and universities.