Justin Miller

Massachusetts Institute of Technology, Marquette University, Ford Motor Company (United States)

Papers

Total Citations

H-Index

About

Justin Miller’s research sits at the intersection of robotics, motion planning, and human-robot interaction, with a growing focus on the security of autonomous systems. His most influential work, “Motion planning with diffusion maps” (28 citations), introduces a novel framework for on-demand path planning in dynamic environments. By encoding pairwise cost-to-go into a potential function, Miller’s method enables robots to efficiently navigate spaces shared with people, addressing a core challenge in real-world deployment. In “A Case Study on the Cybersecurity of Social Robots” (22 citations), he pioneers a critical examination of vulnerabilities in companion robots, highlighting risks as these devices become intimate parts of our homes. This work bridges robotics and cybersecurity, underscoring the need for safe human-robot interaction. More recently, “Planning Beyond The Sensing Horizon Using a Learned Context” (2 citations) tackles last-mile delivery, proposing algorithms that operate without pre-mapped environments—a step toward scalable, economically viable autonomous logistics. Miller’s contributions are shaping how robots plan, adapt, and remain secure in human-centered spaces, making his research essential for students and engineers building the next generation of autonomous systems.