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About

Alessia Li Noce is a roboticist specializing in legged locomotion, traversability analysis, and learning-based control for unstructured environments. Her research focuses on enabling quadruped robots—such as the Mini Cheetah—to navigate challenging terrains like landslide zones safely and autonomously. In her most cited work (2020, 12 citations), she introduced an innovative method for generating traversability maps tailored to different robotic platforms, optimizing path planning for heterogeneous robot teams. She later advanced attitude control in the Mini Cheetah by combining Model Predictive Control with a reward-based feed-forward controller using Self Organizing Feature Maps (2022, 8 citations). Her 2021 study (7 citations) assessed the Mini Cheetah’s navigation capabilities specifically for landslide monitoring, demonstrating practical applications in disaster risk assessment. More recently, she developed neural-network-based controllers for stable two-leg balancing (2024, 6 citations) and addressed critical safety guarantees in learning-based control for legged robots (2024, 5 citations). With over 38 total citations, Li Noce’s work bridges the gap between robust control theory and real-world deployment, making her a rising voice in safe, adaptive legged robotics.