Puze Liu

Puze joined Intelligent Autonomous Systems in Aug 2019 as a Ph.D. student. He work with Davide Tateo and Jan Peters within the research group Safe and Reliable Robot Learning. His research interests include robotics, reinforcement learning, controlling, and human-robot collaboration. Puze is looking for new approaches for robots to learn collaborative tasks that enable robots to assist 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 focused 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.

Key References

    •       Bib
      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.
    •     Bib
      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).
    •     Bib
      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).
    --- Best Paper Award Finalist ---
    •       Bib
      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).
    --- Best Entertainment and Amusement Paper Award Finalist ---
    •     Bib
      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).
    •   Bib
      Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (in press). Composable energy policies for reactive motion generation and reinforcement learning, International Journal of Robotics Research (IJRR).
    •     Bib
      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).

Supervision: 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
2022Project-Haoran DingLearning the Residual Dynamics using Extended Kalman Filter for Puck Tracking
2022ProjectDavide TateoAndreas Seidl Fernandez, Joshua JohannsonLegged Locomotion for Quadruped Robots
2020ProjectDavide TateoVerena Sieburgur, Chen XueBayesian Optimization for System Identification in Robot Air Hockey
2020ProjectJulen UrainNiklas Babendererde, Jiawei HuangBenchmarking Multi-Arm Bandit & Black Box optimization(DFO) 4 Grasping
2019ProjectDavide TateoPatrick LutzRobot Air-Hockey