Max Robotics — Global Service Robot Directory + US Certification

About

Ben Agro is a robotics researcher whose work lies at the intersection of task and motion planning (TAMP) and globally optimal state estimation. His major contributions include pioneering learning-based search strategies for TAMP with streams, a framework that integrates symbolic planning with continuous motion optimization. His most-cited paper, "Learning to Search in Task and Motion Planning With Streams" (2023), has garnered 22 citations and addresses the challenge of efficiently navigating hybrid discrete-continuous planning spaces. Additionally, Agro has advanced the field of state estimation by developing methods for automatically tightened semidefinite relaxations, enabling globally optimal solutions without the need for handcrafted constraints—a significant step toward robust, certifiable robotics. His work on this topic (2024, 8 citations) promises to make optimization-based estimation more accessible and reliable. With a growing citation record and contributions that bridge planning and perception, Agro is establishing himself as a rising figure in robotics, pushing the boundaries of both algorithmic efficiency and theoretical guarantees for autonomous systems.