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

1. •       Bib
     Liu, P.; Bou-Ammar H.; Peters, J.; Tateo D. (conditionally accepted). Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications, Submitted to the IEEE Transactions on Robotics (T-Ro).
2. •       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.
3. •     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).
4. •     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 ---
5. •       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 ---
6. •     Bib
     Guenster, J.; Liu, P.; Peters, J.; Tateo, D. (2024). Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning, Proceedings of the Conference on Robot Learning (CoRL).
7. •     Bib
     Kicki, P.; Tateo; D., Liu, P.; Guenster, J.; Peters, J.; Walas, K. (2024). Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning, Proceedings of the Conference on Robot Learning (CoRL).
8. •       Bib
     Gu, S.; Liu, P.; Kshirsagar, A.; Chen, G.; Peters, J.; Knoll, A. (2024). ROSCOM: Robust Safe Reinforcement Learning on Stochastic Constraint Manifolds, IEEE Transactions on Automation Science and Engineering (T-ASE).
9. •     Bib
     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.
10. •     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).
11. •     Bib
      Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2023). 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).