Giorgia Nadizar

About

Giorgia Nadizar is a rising star in embodied AI and evolutionary robotics, whose work bridges the gap between complex neural control and the physical constraints of soft robots. Her research centers on neuroevolution, graph-based genetic programming, and morphological development, with a particular focus on modular soft robots—deformable, voxel-based agents that can exhibit rich dynamic behaviors. Nadizar’s most cited paper, “Naturally Interpretable Control Policies via Graph-Based Genetic Programming” (2024, 21 citations), introduces a breakthrough approach for generating transparent, human-readable control policies, addressing the critical need for interpretability in autonomous systems. She has also made significant contributions to neural network pruning, demonstrating how removing unnecessary neurons and connections can yield more robust and efficient controllers for soft robots (2021, 15 citations; 2022, 11 citations). Her work systematically compares evolved neural network models (2023, 12 citations) and explores the impact of body material properties on neuroevolution (2022, 10 citations), revealing how physical embodiment shapes learning. Nadizar’s research on morphological development schedules (2022, 11 citations) and totipotent neural controllers (2024, 3 citations) pushes the boundaries of body–brain co-evolution, drawing inspiration from biological development. With over 100 total citations and a rapidly growing publication record, Nadizar is establishing herself as a key figure in creating more interpretable, efficient, and physically grounded artificial agents.

Research Focus

Key Achievements

Top Papers

1. 1
   Naturally Interpretable Control Policies via Graph-Based Genetic Programming

   21 citations · 2024
2. 2
   On the effects of pruning on evolved neural controllers for soft robots

   15 citations · 2021
3. 3
   An experimental comparison of evolved neural network models for controlling simulated modular soft robots

   12 citations · 2023
4. 4
   Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

   11 citations · 2022
5. 5
   On the Schedule for Morphological Development of Evolved Modular Soft Robots

   11 citations · 2022
6. 6
   On the impact of body material properties on neuroevolution for embodied agents

   10 citations · 2022
7. 7
   A Fully-distributed Shape-aware Neural Controller for Modular Robots

   9 citations · 2023
8. 8
   GP for Continuous Control: Teacher or Learner? The Case of Simulated Modular Soft Robots

   8 citations · 2024
9. 9
   The Role of Stepping Stones in MAP-Elites: Insights from Search Trajectory Networks

   4 citations · 2025
10. 10
    Totipotent neural controllers for modular soft robots: Achieving specialization in body–brain co-evolution through Hebbian learning

    3 citations · 2024

Key Collaborators

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