Max Robotics — Global Service Robot Directory + US Certification

About

Kevin Seppi’s research sits at the intersection of robotics, artificial intelligence, and human-robot interaction, with a focus on enabling robots to understand and execute complex, human-informed navigation tasks. His key contributions center on developing algorithms that allow robots to optimize multiple, often conflicting objectives—such as minimizing path length while maximizing information gain or avoiding risk—as demonstrated in his work on the MORRF (multi-objective motion planning) framework. A major thread in Seppi’s work is making robot path-planning responsive to human intent, particularly through topological reasoning. His highly cited paper on “Homotopy-aware RRT*” (with over 30 citations) introduced a method for robots to follow human-specified topological instructions—like “go around the obstacle from the left”—while still optimizing their path. This work, along with his research on informative path planning under human constraints and translating natural language instructions into homotopic requirements, directly addresses the challenge of enabling untrained users to guide robot behavior without needing to understand underlying algorithms. Through these contributions, Seppi has advanced the practical goal of creating collaborative robots that are both efficient and intuitively controllable.