David K. Han

Dr. David K. Han

U.S. Army Research Laboratory

Talk Title: AI/ML for the US Army’s Terrestrial Robotics






David Han is the senior scientist in Artificial Intelligence (AI) in CISD of the Army Research Lab. He also co-chairs the Intelligent Robotics and Autonomous Systems Intergovernmental Working Group under White House Council of Advisors on S&T. He is an ASME fellow and an IEEE senior member. Dr. Han received BS from Carnegie-Mellon University, and MSE and PhD from Johns Hopkins University. He was with JHU Applied Physics Lab as a senior professional staff. He had been with the University of Maryland at College Park as a visiting associate professor, and also he was the Distinguished IWS Chair Professor at the US Naval Academy. From 2012 to 2014 he served as the Deputy Director of Research of the ONR overseeing portfolio of over $900 million. From 2014 to 2016, David was the Associate Director for Basic Research in AI and Robotics at ASD (R&E) responsible for developing long term DoD vision for AI and robotics research. Dr. Han has authored/coauthored over 80 peer-reviewed papers in machine learning.


With affordable sensors and mobility, robots are no longer confined in isolated and predefined environments. There is a proliferation of flying drones for a variety of applications, and the same can be said of autonomous marine robotics. For terrestrial robots, however, only very limited number of applications have been found so far. If fact, most of the ground robots are not autonomous, but remotely operated.  This is due to limitations of current Artificial Intelligence (AI) technologies in dealing with physical environments, particularly the terrain. US Army needs new AI methodologies to enable ground robotic agents to operate in these challenging environments. This talk will provide a brief background of these AI limitations on terrestrial robotics, and will proceed to highlight the U.S. Army Research Laboratory’s (ARL) Essential Research Area (ERA) on Artificial Intelligence & Machine Learning (AI/ML) to seek AI techniques to assist teams of soldiers and autonomous agents in dynamic, uncertain, complex operational environments. Three specific research gaps will be examined: (i) Learning in Complex Data Environments, (ii) Resource-constrained AI Processing at the Point-of-Need and (iii) Generalizable & Predictable AI. The talk will also outline ARL’s current internal research efforts over the next 3-5 years, addressing the Chief of Staff of the Army (CSA) Modernization Priorities for Next Generation Combat Vehicles (NGCV).