Papers
4
Total Citations
39
H-Index
3
About
Nicolas Duminy is a robotics researcher whose work sits at the intersection of developmental robotics, intrinsic motivation, and hierarchical learning. His research focuses on enabling robots to autonomously acquire increasingly complex behavioral repertoires through curiosity-driven exploration and automatic curriculum learning — mechanisms that mirror how humans and animals naturally develop skills over time. Duminy's most influential contribution, "Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner" (2019, 20 citations), addresses the challenge of teaching robots to master hierarchically organized, high-dimensional task spaces by sequencing motor policies intelligently. This work has shaped thinking around how robots can tackle complex, structured environments without exhaustive hand-coded programming. His subsequent work (2021) extended these ideas by integrating social guidance with intrinsic motivation, demonstrating that robots can benefit from human interaction while remaining self-directed learners. Early contributions involving the Poppy humanoid robot (2016) established practical foundations for strategic, interactive learning frameworks. Across his body of work, Duminy has championed the idea that robots need not be explicitly programmed for every task — instead, principled curiosity and social scaffolding can drive genuine developmental growth, making his research particularly valuable for those pursuing autonomous, adaptable robotic systems.
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