Victor Hwang

Papers

Total Citations

H-Index

About

Victor Hwang’s research sits at the intersection of robot motion planning, manipulation, and heuristic search, with a focus on enabling robots to operate efficiently in complex, high-dimensional spaces. His major contributions include pioneering methods for constrained manipulation—such as opening doors or drawers—by learning from demonstrations, which significantly reduces the difficulty of sampling states on constrained manifolds. His work on Multi-Heuristic A* (MHA*) introduced a novel framework that simultaneously leverages multiple, arbitrarily inadmissible heuristics alongside a consistent one, simplifying heuristic design while guaranteeing complete and bounded suboptimal solutions. Hwang also advanced lifelong planning through lazy validation of Experience Graphs, allowing robots to reuse and validate prior motion plans in static environments, dramatically cutting computation time for repeated tasks. With over 70 citations across his most-cited papers, his research has directly influenced how robots plan for assembly, sorting, and other repetitive operations. Notably, his 2015 paper on learning to plan for constrained manipulation remains a foundational reference for researchers tackling mobile manipulation challenges. Hwang’s work is essential reading for anyone interested in making robots more autonomous and practical in real-world settings.