Papers
16
Total Citations
230
H-Index
10
About
Daniele Caligiore’s research lies at the intersection of cognitive robotics, developmental psychology, and reinforcement learning, with a focus on building bio-inspired architectures that model how humans and robots learn, transfer, and generalize skills. His major contributions include a hierarchical reinforcement learning framework that enables knowledge transfer between skills when solving multiple tasks—a key challenge in both AI and motor control. He has also pioneered neurorobotic models of mental rotation, demonstrating how embodiment and decision-making shape cognitive processes in humanoid robots. Notably, his work on “Transitional Wearable Companions” proposes soft interactive social robots to improve social skills in children with autism spectrum disorder, reflecting a strong translational impact. Across his most cited papers, which collectively have garnered over 200 citations, Caligiore has advanced understanding of how rhythmic and discrete manipulation movements interplay during development, and how assimilation and accommodation in reaching tasks relate to autism. His models integrate kinematic and dynamic control, central pattern generators, and policy-search reinforcement learning, offering a unified view of motor and cognitive development. For students and researchers, Caligiore’s work exemplifies how computational modeling and robotics can illuminate fundamental questions about learning, development, and social interaction.
Research Focus
Key Achievements
Top Papers
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- 5Modelling mental rotation in cognitive robots18 citations · 2013
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