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About

Miguel Zamora is a robotics researcher whose work spans motion planning, legged locomotion, and autonomous systems, with a career arc that reflects the field's evolution over more than two decades. His early contributions in the early 2000s addressed foundational challenges in mobile robotics, including ultrasonic sensor-based map building and evolutionary approaches to behavior fusion in autonomous agents. His more recent and highly impactful work has focused on optimization and learning for complex robotic systems. His 2020 paper on simultaneous grasp and motion planning — earning 42 citations — introduced a multi-level optimization framework that elegantly unifies trajectory planning and grasping decisions, a departure from conventional sequential approaches. He has since made significant contributions to legged locomotion, combining reinforcement learning with model-based optimal control to produce versatile, robust movement strategies, work that has accumulated 35 citations since 2023. His 2024 research on deep compliant control further refines legged robot behavior by encouraging more natural, human-like responses to disturbances. Across these threads, Zamora consistently bridges theoretical rigor with real-world applicability, establishing himself as a thoughtful contributor to the intersection of optimization, learning, and physical robotics.