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About

Edgar Granados is a robotics researcher whose work sits at the intersection of motion planning, machine learning, and autonomous systems. His research focuses primarily on sampling-based motion planning and kinodynamic planning, areas where he has made meaningful contributions to improving both computational efficiency and path quality for robotic and vehicular navigation. Granados is perhaps best known for his comprehensive survey on integrating machine learning with sampling-based motion planning (2022), which has garnered 14 citations and serves as a valuable reference for researchers navigating this rapidly evolving field. His work on learned goal-reaching controllers for kinodynamic planners demonstrates a practical approach to enhancing planning performance by leveraging reinforcement learning to guide control selection during path expansion. Building on this, his terrain-aware controller research extends these ideas to challenging uneven environments, reflecting a commitment to real-world applicability. Beyond planning, Granados has explored differentiable physics for mobile robot model identification, showcasing his breadth across data-driven robotics. With a growing body of cited work published across consecutive years, he represents an emerging voice in autonomous systems research whose contributions are increasingly shaping how robots learn to navigate complex, dynamic environments.