Davide Tateo

Research Interests

Reinforcement Learning: Safe RL, Deep RL Robotics: fast and reactive motion planning, locomotion

Affiliation

TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department

Contact

davide.tateo@tu-darmstadt.de
Room E303, Building S2|02, TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
+49-6151-16-20811

Davide Tateo is a postdoctoral researcher and Safe and Reliable Robot Learning Research Group Leader in the Intelligent Autonomous Systems group. Davide joined the lab in April 2019 after receiving his Ph.D. in Information Technology from Politecnico di Milano (Milan, Italy) in February 2019. Please see his Curriculum Vitae to know all about him.

Software

MushroomRL: A Python Reinforcement Learning Library, developed by Carlo D'Eramo and me, that provides both a clear interface to various benchmarking environments and simulators and implementation of many classical and deep reinforcement learning algorithms.

The main goal of his research group is to develop learning algorithms that can be deployed on real systems. To achieve this objective, the group focuses on fundamental properties of the learning algorithm, such as acting under (safety) constraints.

During his Ph.D. research, Davide worked under the supervision of Prof. Andrea Bonarini and prof. Marcello Restelli focusing particularly on Hierarchical and Inverse Reinforcement Learning. He also co-developed MushroomRL, a Reinforcement Learning Python library.

In the first years of his stay at IAS, he worked on the SKILLS4ROBOTS project, whose objective was to develop humanoid robots that can acquire and improve a rich set of motor skills. Currently, he is involved in a wide variety of projects: the collaborative KIARA project to bring advanced manipulation skills to risky scenarios, the DeepWalking project, to learn human gaits from demonstrations, and the INTENTION project, to develop legged robot locomotion exploiting active perception techniques.

Publications

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    Kicki, P.; Liu, P.; Tateo, D.; Bou Ammar, H.; Walas, K.; Skrzypczynski, P.; Peters, J. (2024). Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks, IEEE Transactions on Robotics (T-Ro), and Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 40, pp.277-297.
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    Al-Hafez, F.; Zhao, G.; Peters, J.; Tateo, D. (2024). Time-Efficient Reinforcement Learning with Stochastic Stateful Policies, International Conference on Learning Representations (ICLR).
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    Lach, L.; Haschke, R.; Tateo, D.; Peters, J.; Ritter, H.; Sol, J.; Torras, C. (2024). Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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    Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2023). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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    Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR).
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    Urain, J.; Tateo, D.; Peters, J. (2023). Learning Stable Vector Fields on Lie Groups, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE R-AL Track.
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    Bjelonic, F.; Lee, J.; Arm, P.; Sako, D.; Tateo, D.; Peters, J.; Hutter, M. (2023). Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators, IEEE Robotics and Automation Letters (R-AL), 8, 3, pp.1611-1618.
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    Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). Least Squares Inverse Q-Learning, European Workshop on Reinforcement Learning (EWRL).
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    Lach, L. M.; Haschke, R.; Tateo, D.; Peters, J.; Ritter, H.; Borras, J.; Torras, C. (2023). Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot, TouchProcessing workshop at NeurIPS..
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    Al-Hafez, F.; Zhao, G.; Peters, J.; Tateo, D. (2023). LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion, Robot Learning Workshop, Conference on Neural Information Processing Systems (NeurIPS).
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    Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning, Algorithms, 15, 3, pp.81.
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    Akrour, R.; Tateo, D.; Peters, J. (2022). Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.6795-6806.
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    Memmel, M.; Liu, P.; Tateo, D.; Peters, J. (2022). Dimensionality Reduction and Prioritized Exploration for Policy Search, 25th International Conference on Artificial Intelligence and Statistics (AISTATS).
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    Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Peters, J.; Chalvatzaki, G.; (2022). Regularized Deep Signed Distance Fields for Reactive Motion Generation, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Peters, J.; Chalvatzaki, G. (2022). ReDSDF: Regularized Deep Signed Distance Fields for Robotics, ICRA 2022 workshop on Motion Planning with Implicit Neural Representations of Geometry.
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    Carvalho, J., Tateo, D., Muratore, F., Peters, J. (2021). An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients, International Joint Conference on Neural Networks (IJCNN).
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    Liu, P.; Tateo, D.; Bou-Ammar, H.; Peters, J. (2021). Efficient and Reactive Planning for High Speed Robot Air Hockey, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Liu, P.; Tateo, D.; Bou-Ammar, H.; Peters, J. (2021). Robot Reinforcement Learning on the Constraint Manifold, Proceedings of the Conference on Robot Learning (CoRL).
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    D`Eramo, C.; Tateo, D.; Bonarini, A.; Restelli, M.; Peters, J. (2020). Sharing Knowledge in Multi-Task Deep Reinforcement Learning, International Conference in Learning Representations (ICLR).
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    Urain, J.; Ginesi, M.; Tateo, D.; Peters, J. (2020). ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows, IEEE/RSJ International Conference on Intelligent Robots and Systems.
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    Urain, J.; Tateo, D.; Ren, T.; Peters, J. (2020). Structured policy representation: Imposing stability in arbitrarily conditioned dynamic systems, NeurIPS 2020, 3rd Robot Learning Workshop, pp.7.
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    Tateo, D. (2019). Building structured hierarchical agents, Ph.D. Thesis.
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    Beretta, C.; Brizzolari, C.; Tateo, D.; Riva, A.; Amigoni F. (2019). A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths, European Conference on Mobile Robots (ECMR).
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    Tateo, D.; Erdenlig, I. S.; Bonarini, A. (2019). Graph-Based Design of Hierarchical Reinforcement Learning Agents, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.
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    Akrour, R.; Tateo, D.; Peters, J. (2019). Towards Reinforcement Learning of Human Readable Policies, ECML/PKDD Workshop on Deep Continuous-Discrete Machine Learning.
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    Tateo, D.; Banfi, J.; Riva, A.; Amigoni, F.; Bonarini, A. (2018). Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal with It, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018), pp.4735-4742.
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    Tateo, D.; D'Eramo, C.; Nuara, A.; Bonarini, A.; Restelli, M. (2017). Exploiting structure and uncertainty of Bellman updates in Markov decision processes, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
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    Tateo, D.; Pirotta, M.; Restelli, M.; Bonarini, A. (2017). Gradient-based minimization for multi-expert Inverse Reinforcement Learning, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).