‪Riccardo Zamboni‬ - ‪Google Scholar‬

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202220231 4

Co-authors

Mitsuhiro HayashibeProfessor, Tohoku University, INRIA, Jikei Univ. School of Medicine, Univ. of TokyoVerified email at inria.fr
Owaki DaiTohoku UniversityVerified email at tohoku.ac.jp

Riccardo Zamboni

Riccardo Zamboni

Politecnico di Milano

Verified email at polimi.it - Homepage

Reinforcement Learning Machine Learning Artificial Intelligence


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Adaptive and energy-efficient optimal control in CPGs through tegotae-based feedback R Zamboni, D Owaki, M Hayashibe Frontiers in Robotics and AI 8, 632804, 2021	5	2021
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning R Zamboni, AM Metelli, M Restelli Advances in Neural Information Processing Systems 36, 2024		2024
Energy Efficiency Analysis of the Tegotae Approach for Bio-inspired Hopping R Zamboni, D Owaki, M Hayashibe 9ᵗʰ International Symposium on Adaptive Motion of Animals and Machines (AMAM …, 2019		2019
Introducing a proprio-ceptive feedback in the bio-inspired Tegotae control approach: enhanced learning and energy efficiency R ZAMBONI Politecnico di Milano, 2018		2018

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Articles 1–4