Leonardo Lamanna

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About

Leonardo Lamanna is an emerging researcher working at the intersection of artificial intelligence, robotics, and symbolic planning. His work focuses on the critical challenge of enabling robotic agents to operate intelligently in unknown or partially observable environments, bridging the gap between abstract symbolic reasoning and real-world physical deployment. His most notable contribution, "Online Grounding of Symbolic Planning Domains in Unknown Environments" (2022), addresses a fundamental bottleneck in autonomous robotics: how an agent can dynamically ground abstract planning knowledge into an unfamiliar environment through active exploration, rather than relying on pre-specified world models. This research represents a meaningful step toward more flexible and autonomous robotic systems capable of adapting their planning strategies on the fly. While still building his citation profile — with his key work accumulating 3 citations since 2022 — Lamanna's research tackles problems of significant long-term importance as AI systems are increasingly deployed in unstructured, real-world settings. His contributions are particularly relevant to researchers working on task and motion planning, knowledge representation, and the broader goal of developing truly autonomous robots that can reason, explore, and act without extensive human pre-configuration.