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About

Leonardo de Lellis Rossi is an emerging researcher at the intersection of cognitive robotics, developmental learning, and artificial intelligence. His work centers on enabling autonomous robots to learn and adapt in complex environments, drawing inspiration from human cognitive development — most notably Piagetian theories of constructive learning — to design more capable artificial agents. Rossi's most-cited contribution, "Piagetian Experiments to DevRobotics" (2023, 9 citations), bridges developmental psychology and robotics, advancing the vision of machines that genuinely emulate human-like reasoning and behavior. His follow-up work, "A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents" (2024, 4 citations), tackles the pressing challenge of continuous learning in AI systems, proposing novel architectures for agents operating in increasingly complex settings. Complementing this, his research on incremental sensorimotor learning in humanoid robots and attentional space management in mobile platforms demonstrates a sustained focus on scalable, biologically informed learning frameworks. His 2025 work on drive-based reinforcement learning signals a maturing research agenda exploring how internal motivational structures can optimize autonomous decision-making. With a growing body of work across developmental robotics and cognitive AI, Rossi represents a promising voice shaping the next generation of truly adaptive intelligent systems.