Exploiting structure and uncertainty of Bellman updates in Markov decision processes. - Davide Tateo; Carlo D'Eramo; Alessandro Nuara; Marcello Restelli; Andrea Bonarini (SSCI, 2017)

Gradient-based minimization for multi-expert Inverse Reinforcement Learning. - Davide Tateo; Matteo Pirotta; Marcello Restelli; Andrea Bonarini (SSCI, 2017)

Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal With It. - Davide Tateo; Jacopo Banfi; Alessandro Riva; Francesco Amigoni; Andrea Bonarini (AAAI, 2018)

A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths. - Carolina Beretta; Cecilia Brizzolari; Davide Tateo; Alessandro Riva; Francesco Amigoni (ECMR, 2019)

Graph-Based Design of Hierarchical Reinforcement Learning Agents. - Davide Tateo; Idil Su Erdenlig; Andrea Bonarini (IROS, 2019)

Building structured hierarchical agents. - Davide Tateo (2019)

Sharing Knowledge in Multi-Task Deep Reinforcement Learning. - Carlo D'Eramo; Davide Tateo; Andrea Bonarini; Marcello Restelli; Jan Peters 001 (ICLR, 2020)

ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows. - Julen Urain; Michele Ginesi; Davide Tateo; Jan Peters 001 (IROS, 2020)

Reinforcement Learning from a Mixture of Interpretable Experts. - Riad Akrour; Davide Tateo; Jan Peters 001 (CoRR, 2020)

Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems. - Julen Urain; Davide Tateo; Tianyu Ren; Jan Peters 001 (CoRR, 2020)

Robot Reinforcement Learning on the Constraint Manifold. - Puze Liu; Davide Tateo; Haitham Bou-Ammar; Jan Peters 001 (CoRL, 2021)

An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients. - João Carvalho; Davide Tateo; Fabio Muratore; Jan Peters 001 (IJCNN, 2021)

Efficient and Reactive Planning for High Speed Robot Air Hockey. - Puze Liu; Davide Tateo; Haitham Bou-Ammar; Jan Peters 001 (IROS, 2021)

MushroomRL: Simplifying Reinforcement Learning Research. - Carlo D'Eramo; Davide Tateo; Andrea Bonarini; Marcello Restelli; Jan Peters 001 (J. Mach. Learn. Res., 2021)

Dimensionality Reduction and Prioritized Exploration for Policy Search. - Marius Memmel; Puze Liu; Davide Tateo; Jan Peters 001 (AISTATS, 2022)

Regularized Deep Signed Distance Fields for Reactive Motion Generation. - Puze Liu; Kuo Zhang; Davide Tateo; Snehal Jauhri; Jan Peters 001; Georgia Chalvatzaki (IROS, 2022)

Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. - Simone Parisi; Davide Tateo; Maximilian Hensel; Carlo D'Eramo; Jan Peters 001; Joni Pajarinen (Algorithms, 2022)

Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping. - Davide Tateo; Davide Antonio Cucci; Matteo Matteucci; Andrea Bonarini (CoRR, 2022)

Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts. - Riad Akrour; Davide Tateo; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Learning Stable Vector Fields on Lie Groups. - Julen Urain; Davide Tateo; Jan Peters 001 (IEEE Robotics Autom. Lett., 2022)

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. - Firas Al-Hafez; Davide Tateo; Oleg Arenz; Guoping Zhao; Jan Peters 001 (ICLR, 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 001; Georgia Chalvatzaki (ICRA, 2023)

LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion. - Firas Al-Hafez; Guoping Zhao; Jan Peters 001; Davide Tateo (CoRR, 2023)

Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot. - Luca Lach; Robert Haschke; Davide Tateo; Jan Peters 001; Helge J. Ritter; Júlia Borràs Sol; Carme Torras (CoRR, 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 001; Marco Hutter 001 (IEEE Robotics Autom. Lett., 2023)

One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion. - Nico Bohlinger; Grzegorz Czechmanowski; Maciej Piotr Krupka; Piotr Kicki; Krzysztof Walas; Jan Peters 001; Davide Tateo (CoRL, 2024)

Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning. - Jonas Günster; Puze Liu; Jan Peters 001; Davide Tateo (CoRL, 2024)

Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning. - Piotr Kicki; Davide Tateo; Puze Liu; Jonas Günster; Jan Peters 001; Krzysztof Walas (CoRL, 2024)

Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion. - Henri-Jacques Geiß; Firas Al-Hafez; André Seyfarth; Jan Peters 001; Davide Tateo (Humanoids, 2024)

Time-Efficient Reinforcement Learning with Stochastic Stateful Policies. - Firas Al-Hafez; Guoping Zhao; Jan Peters 001; Davide Tateo (ICLR, 2024)

Zero-Shot Transfer of a Tactile-based Continuous Force Control Policy from Simulation to Robot. - Luca Lach; Robert Haschke; Davide Tateo; Jan Peters 001; Helge J. Ritter; Júlia Borràs Sol; Carme Torras (IROS, 2024)

A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics. - Puze Liu; Jonas Günster; Niklas Funk; Simon Gröger; Dong Chen; Haitham Bou-Ammar; Julius Jankowski; Ante Maric; Sylvain Calinon; Andrej Orsula; Miguel S. Olivares-Méndez; Hongyi Zhou; et al. (NeurIPS, 2024)

A Holistic Concept on AI Assistance for Robot-Supported Reconnaissance and Mitigation of Acute Radiation Hazard Situations. - Kevin Daun; Frederik Bark; Davide Tateo; Jan Peters 001; Johannes Heinlein; Janine Wendt; Nils Heidemann; Ivana Kruijff-Korbayová; Stefan Kohlbrecher; Jens Friedrich; Désirée Martin; Michael W. Schmidt; et al. (SSRR, 2024)

ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning. - Christopher E. Mower; Yuhui Wan; Hongzhan Yu; Antoine Grosnit; Jonas Gonzalez-Billandon; Matthieu Zimmer; Jinlong Wang; Xinyu Zhang; Yao Zhao; Anbang Zhai; Puze Liu; Davide Tateo; et al. (CoRR, 2024)

Energy-based Contact Planning under Uncertainty for Robot Air Hockey. - Julius Jankowski; Ante Maric; Puze Liu; Davide Tateo; Jan Peters 001; Sylvain Calinon (CoRR, 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 001 (IEEE Trans. Robotics, 2024)

Safe and Efficient Path Planning Under Uncertainty via Deep Collision Probability Fields. - Felix Herrmann; Sebastian Zach; Jacopo Banfi; Jan Peters 001; Georgia Chalvatzaki; Davide Tateo (IEEE Robotics Autom. Lett., 2024)

Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications. - Puze Liu; Haitham Bou-Ammar; Jan Peters 001; Davide Tateo (IEEE Trans. Robotics, 2025)

Towards Safe Robot Foundation Models. - Maximilian Tölle; Theo Gruner; Daniel Palenicek; Jonas Günster; Puze Liu; Joe Watson; Davide Tateo; Jan Peters 001 (CoRR, 2025)

Adaptive Control Based Friction Estimation for Tracking Control of Robot Manipulators. - Junning Huang; Davide Tateo; Puze Liu; Jan Peters 001 (IEEE Robotics Autom. Lett., 2025)

Robust Localization, Mapping, and Navigation for Quadruped Robots. - Dyuman Aditya; Junning Huang; Nico Bohlinger; Piotr Kicki; Krzysztof Walas; Jan Peters 001; Matteo Luperto; Davide Tateo (CoRR, 2025)

Towards Safe Robot Foundation Models Using Inductive Biases. - Maximilian Tölle; Theo Gruner; Daniel Palenicek; Tim Schneider; Jonas Günster; Joe Watson; Davide Tateo; Puze Liu; Jan Peters 001 (CoRR, 2025)