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About

Simone Tolomei is a robotics researcher whose work sits at the intersection of field robotics, ecological monitoring, and reinforcement learning. His primary research focuses on developing legged robotic platforms—specifically quadrupedal robots—for autonomous environmental surveying in European natural habitats. Tolomei’s major contributions include the design and control of Otto, an 8-degree-of-freedom quadrupedal robot driven by Series Elastic Actuators (SEA), which he paired with a reinforcement learning framework for simulated locomotion training. He also led the creation of a labeled dataset for plant detection in Annex I habitats of Italy, captured using the ANYmal C quadruped, enabling robotic monitoring of ecologically significant species. His work on a robotic-assisted framework for standardized forest surveys integrates LiDAR and onboard cameras to assess forest structure and composition. With his most cited paper already accumulating 5 citations in 2025, Tolomei is establishing a strong early-career impact. His research is notable for directly addressing the practical challenges of deploying robots in unstructured, sensitive environments, bridging the gap between robotics and conservation science.