Davide Tateo

According to our database, Davide Tateo authored at least 27 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks.

Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczynski, Jan Peters

IEEE Trans. Robotics, 2024

2023

Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators.

Filip Bjelonic, Joonho Lee, Philip Arm, Dhionis V. Sako, Davide Tateo, Jan Peters, Marco Hutter

IEEE Robotics Autom. Lett., March, 2023

Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot.

Luca Lach, Robert Haschke, Davide Tateo, Jan Peters, Helge J. Ritter, Júlia Borràs Sol, Carme Torras

CoRR, 2023

Time-Efficient Reinforcement Learning with Stochastic Stateful Policies.

Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo

CoRR, 2023

LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion.

Firas Al-Hafez, Guoping Zhao, Jan Peters, Davide Tateo

CoRR, 2023

Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction.

Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Zhiyuan Hu, Jan Peters, Georgia Chalvatzaki

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning.

Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning Stable Vector Fields on Lie Groups.

Julen Urain, Davide Tateo, Jan Peters

IEEE Robotics Autom. Lett., 2022

Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts.

Riad Akrour, Davide Tateo, Jan Peters

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping.

Davide Tateo, Davide Antonio Cucci, Matteo Matteucci, Andrea Bonarini

CoRR, 2022

Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning.

Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Algorithms, 2022

Regularized Deep Signed Distance Fields for Reactive Motion Generation.

Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Jan Peters, Georgia Chalvatzaki

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Dimensionality Reduction and Prioritized Exploration for Policy Search.

Marius Memmel, Puze Liu, Davide Tateo, Jan Peters

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

MushroomRL: Simplifying Reinforcement Learning Research.

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

J. Mach. Learn. Res., 2021

Efficient and Reactive Planning for High Speed Robot Air Hockey.

Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients.

João Carvalho, Davide Tateo, Fabio Muratore, Jan Peters

Proceedings of the International Joint Conference on Neural Networks, 2021

Robot Reinforcement Learning on the Constraint Manifold.

Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems.

Julen Urain, Davide Tateo, Tianyu Ren, Jan Peters

CoRR, 2020

Reinforcement Learning from a Mixture of Interpretable Experts.

Riad Akrour, Davide Tateo, Jan Peters

CoRR, 2020

ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows.

Julen Urain, Michele Ginesi, Davide Tateo, Jan Peters

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Sharing Knowledge in Multi-Task Deep Reinforcement Learning.

Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Building structured hierarchical agents.

Davide Tateo

PhD thesis, 2019

Graph-Based Design of Hierarchical Reinforcement Learning Agents.

Davide Tateo, Idil Su Erdenlig, Andrea Bonarini

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths.

Carolina Beretta, Cecilia Brizzolari, Davide Tateo, Alessandro Riva, Francesco Amigoni

Proceedings of the 2019 European Conference on Mobile Robots, 2019

2018

Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal With It.

Davide Tateo, Jacopo Banfi, Alessandro Riva, Francesco Amigoni, Andrea Bonarini

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Gradient-based minimization for multi-expert Inverse Reinforcement Learning.

Davide Tateo, Matteo Pirotta, Marcello Restelli, Andrea Bonarini

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Exploiting structure and uncertainty of Bellman updates in Markov decision processes.

Davide Tateo, Carlo D'Eramo, Alessandro Nuara, Marcello Restelli, Andrea Bonarini

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017