Sledovat
Davide Tateo
Davide Tateo
Research group leader at Technische Universität Darmstadt
E-mailová adresa ověřena na: ias.informatik.tu-darmstadt.de - Domovská stránka
Název
Citace
Citace
Rok
Sharing knowledge in multi-task deep reinforcement learning
C D'Eramo, D Tateo, A Bonarini, M Restelli, J Peters
8th International Conference on Learning Representations, (ICLR) 2020, Addis …, 2020
144*2020
Mushroomrl: Simplifying reinforcement learning research
C D'Eramo, D Tateo, A Bonarini, M Restelli, J Peters
Journal of Machine Learning Research 22 (131), 1-5, 2021
792021
Robot reinforcement learning on the constraint manifold
P Liu, D Tateo, HB Ammar, J Peters
Conference on Robot Learning, 1357-1366, 2022
552022
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
J Urain, M Ginesi, D Tateo, J Peters
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
532020
Regularized deep signed distance fields for reactive motion generation
P Liu, K Zhang, D Tateo, S Jauhri, J Peters, G Chalvatzaki
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022
342022
Learning-based design and control for quadrupedal robots with parallel-elastic actuators
F Bjelonic, J Lee, P Arm, D Sako, D Tateo, J Peters, M Hutter
IEEE Robotics and Automation Letters 8 (3), 1611-1618, 2023
302023
Continuous action reinforcement learning from a mixture of interpretable experts
R Akrour, D Tateo, J Peters
IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6795 …, 2021
27*2021
Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal with It
D Tateo, J Banfi, A Riva, F Amigoni, A Bonarini
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018), 4735-4742, 2018
272018
Efficient and reactive planning for high speed robot air hockey
P Liu, D Tateo, H Bou-Ammar, J Peters
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
262021
Ls-iq: Implicit reward regularization for inverse reinforcement learning
F Al-Hafez, D Tateo, O Arenz, G Zhao, J Peters
arXiv preprint arXiv:2303.00599, 2023
242023
Learning stable vector fields on lie groups
J Urain, D Tateo, J Peters
IEEE Robotics and Automation Letters 7 (4), 12569-12576, 2022
22*2022
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
P Kicki, P Liu, D Tateo, H Bou-Ammar, K Walas, P Skrzypczyński, J Peters
IEEE Transactions on Robotics 40, 277 - 297, 2023
202023
Locomujoco: A comprehensive imitation learning benchmark for locomotion
F Al-Hafez, G Zhao, J Peters, D Tateo
arXiv preprint arXiv:2311.02496, 2023
202023
Safe reinforcement learning of dynamic high-dimensional robotic tasks: navigation, manipulation, interaction
P Liu, K Zhang, D Tateo, S Jauhri, Z Hu, J Peters, G Chalvatzaki
2023 IEEE International Conference on Robotics and Automation (ICRA), 9449-9456, 2023
192023
Gradient-based minimization for multi-expert inverse reinforcement learning
D Tateo, M Pirotta, M Restelli, A Bonarini
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017
162017
Ros-llm: A ros framework for embodied ai with task feedback and structured reasoning
CE Mower, Y Wan, H Yu, A Grosnit, J Gonzalez-Billandon, M Zimmer, ...
arXiv preprint arXiv:2406.19741, 2024
92024
Long-term visitation value for deep exploration in sparse-reward reinforcement learning
S Parisi, D Tateo, M Hensel, C D’eramo, J Peters, J Pajarinen
Algorithms 15 (3), 81, 2022
92022
Safe reinforcement learning on the constraint manifold: Theory and applications
P Liu, H Bou-Ammar, J Peters, D Tateo
arXiv preprint arXiv:2404.09080, 2024
72024
An empirical analysis of measure-valued derivatives for policy gradients
J Carvalho, D Tateo, F Muratore, J Peters
2021 International Joint Conference on Neural Networks (IJCNN), 1-10, 2021
72021
Towards Reinforcement Learning of Human Readable Policies
R Akrour, D Tateo, J Peters
ECML/PKDD Workshop on Deep Continuous-Discrete Machine Learning, 2019
62019
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Články 1–20