Puze Liu

Quick Info

Research Interests

Robotics, Reinforcement Learning, Control and Optimization, Safe Reinforcement Learning.
Homepage GitHub Google Scholar DBLP

Contact Information

Mail. TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
Office. Room E303, Building S2|02
work+49-6151-16-20811

Puze joined the Intelligent Autonomous Systems Group in Aug 2019 as a Ph.D. student. His research interests include robotics, reinforcement learning, inverse reinforcement learning, and human-robot collaboration. Puze is looking for new approaches for robots to learn collaborative tasks that enable robots assisting humans in different scenarios.

Before starting his Ph.D., Puze completed his Master's degree in Computational Engineering Science at the Technische Universitaet Berlin. During his study, Puze's previous work focusing on robot fundamental algorithms and safety problems during human-robot interaction. His master thesis "Industrial Robot Motion Control depending on the Distance between Human and Robot" was under the supervision of Prof. Dr-Ing. Jörg Krüger and Martin J. Rosenstrauch.

Research Interest

Robotics, Reinforcement Learning, Control and Optimization, Safe Reinforcement Learning.

Key References

  1. Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2022). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, https://arxiv.org/abs/2209.13308, arXiv.   Download Article [PDF]   BibTeX Reference [BibTex]
  2. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  3. 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).   Download Article [PDF]   BibTeX Reference [BibTex] --- Best Paper Award Finalist ---
  4. 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).   Download Article [PDF]   BibTeX Reference [BibTex] Webpage --- Best Entertainment and Amusement Paper Award Finalist ---
  5. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  6. Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2021). Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning, Robotics: Science and Systems (RSS).   Download Article [PDF]   BibTeX Reference [BibTex]

Completed Theses and Projects

StartTypeIn coorperation withStudent(s)TopicDocument
2022Master ThesisDavide Tateo, Georgia ChalvatzakiKuo ZhangLearning Geometric Constraints for Safe Robot Interactionspdf
2021Master ThesisDavide TateoVerena SieburgurDevelopment of a Baseline Agent in Robot Air Hockeypdf
2020Master Thesis-Jianhang HeImitation Learning with Energy Based Modelpdf
2020Master ThesisJulen UrainZhenhui ZhouApproximated Policy Search in Black-Box Optimizationpdf
2022Bachelor ThesisDavide TateoJonas GünsterLearning the Low Level Policy for Robot Air Hockeypdf
2022Integrated Project-Haoran DingLearning the Residual Dynamics using Extended Kalman Filter for Puck Tracking
2022Integrated ProjectDavide TateoAndreas Seidl Fernandez, Joshua JohannsonLegged Locomotion for Quadruped Robots
2020Integrated ProjectDavide TateoVerena Sieburgur, Chen XueBayesian Optimization for System Identification in Robot Air Hockey
2020Integrated ProjectJulen UrainNiklas Babendererde, Jiawei HuangBenchmarking Multi-Arm Bandit & Black Box optimization(DFO) 4 Grasping
2019Integrated ProjectDavide TateoPatrick LutzRobot Air-Hockey

  

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