About

Marco Mirolli is a prominent computational cognitive scientist whose research sits at the intersection of intrinsic motivation, reinforcement learning, cognitive robotics, and the evolution of language and communication. He is perhaps best known for his foundational contributions to intrinsically motivated learning systems, exemplified by his highly cited 2012 edited volume "Intrinsically Motivated Learning in Natural and Artificial Systems" (371 citations), which helped consolidate the field by bridging biological and computational perspectives on curiosity-driven behavior. Mirolli's work explores how autonomous agents — both robotic and simulated — can discover and pursue their own goals without relying on externally assigned rewards. His GRAIL architecture (2016, 91 citations) represents a landmark technical achievement in this direction, enabling robots to independently discover environmental affordances and self-organize learning objectives. Alongside this, his investigations into Vygotskyan cognitive robotics highlight how language functions as a cognitive scaffold, enriching our understanding of human-like reasoning in artificial systems. His early explorations into evolving internal reinforcers (2007) laid important groundwork for subsequent neuroevolutionary approaches to skill acquisition. Across more than a decade of research, Mirolli has consistently shaped how the scientific community thinks about open-ended autonomous development, accumulating over 1,100 citations and establishing himself as an influential voice in both robotics and cognitive science.

Research Focus

Key Achievements

19
H-Index
29
Papers
1,406
Total Citations
48
Avg Citations/Paper
🏆 Most Cited Paper
Intrinsically Motivated Learning in Natural and Artificial Systems
371 citations · 2012
📈 Most Prolific Year: 2014 (6 Papers)
🤝 Key Collaborators: 57
🏛 Institutions: Institute of Cognitive Sciences and Technologies, National Research Council, National Academies of Sciences, Engineering, and Medicine, Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago