LM Robotics Seminar: Human-Centered Machine Learning for Autonomous Navigation

Friday, October 8, 2021
2:00 p.m.
JMP 2121

Human-Centered Machine Learning for Autonomous Navigation

Maggie Wigness

Computer Scientist
Combat Capabilities Development Command
Army Research Laboratory

Streaming Link: https://vid.umd.edu/detsmediasite/Play/a4e6bd030a394ab7a9381380739fe1b71d


Data-driven AI/ML techniques have advanced significantly to automate skills such as detection, target recognition, and mobility. Yet, there are many applications, such as military operation or humanitarian assistance and disaster relief, where it is highly likely that the operating domain will depict some distributional shift from that in which a system was trained. Under these scenarios, the design of AI systems that can be trained or refined quickly, potentially in real-time, becomes critically important to ensure safety and success. I will discuss how we are incorporating learning from human demonstration to address the need for efficient, on-line learning in the field. I will specifically focus on several approaches to learn navigation behaviors for unmanned ground robots using teleoperation demonstrations, allowing for non-expert users to refine ML reward functions with relatively little effort.


Maggie Wigness is a researcher in the Computational and Information Sciences Directorate at DEVCOM Army Research Laboratory (ARL). She earned her PhD in Computer Science from Colorado State University in 2015. Maggie has led and shaped research directions in many ARL collaborative alliances as a Capability Lead for the Robotics Collaborative Technology Alliance (RCTA), a Technical Integration Lead for the Scalable, Adaptive, and Resilient Autonomy (SARA) Collaborative Research Alliance (CRA), and as the Deputy Collaborative Alliance Manager for the Internet of Battlefield Things (IoBT) CRA. Maggie's current research interests are in the cross section of machine learning, computer vision, and robot autonomy, with a specific focus on adaptive and on-line learning from human demonstration.

Contact: appicard@umd.edu

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