Ph.D. Student at the University of Luebeck

Short bio: Mr. Honghu Xue investigates in his doctoral thesis deep reinforcement learning approaches for planning and control. His methods are applied to mobile robots and the FRANKA EMIKA robot arm. He started his thesis in March 2019.
Honghu Xue received his M.Sc. in Embedded Systems Engineering at Albert-Ludwigs-University of Freiburg with the study focus on Reinforcement Learning, Machine Learning and AI.
Research Interests
- Deep Reinforcement Learning: Model-Based RL, Sample-efficient RL, Long-time-horizon RL, Efficient Exploration Strategies in MDP, Distributional RL, Policy Search.
- Deep & Machine Learning: Learning Transition Model in MDP (featuring visual input and modelling the stochasticity of the environment), Super-resolution Image using DL, Time-Sequential Model for Partial Observability.
Contact & Quick Links
M.Sc. Honghu Xue
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since March 2019.
Ratzeburger Allee 160,
23562 Lübeck,
Deutschland
Phone: +49 451 3101 – 5213
Email: xue@rob.uni-luebeck.de
Web:https://www.rob.uni-luebeck.de/index.php?id=460
CV of M.Sc. Honghu Xue
DBLP
Frontiers Network
Github
Google Citations
LinkedIn
ORCID
Rearch Gate
Meeting Notes
Publcations
2021 |
|
Xue, Honghu; Herzog, Rebecca; Berger, Till M.; Bäumer, Tobias; Weissbach, Anne; Rueckert, Elmar Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation Journal Article In: Frontiers in Robotics and AI , vol. 8, 2021, ISSN: 2296-9144. @article{Rueckert2021, In medical tasks such as human motion analysis, computer-aided auxiliary systems have become preferred choice for human experts for its high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives(ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with10participants. The results validate ProMPs as a robust and effective feature extractor for human motions. | ![]() |
Cansev, Mehmet Ege; Xue, Honghu; Rottmann, Nils; Bliek, Adna; Miller, Luke E.; Rueckert, Elmar; Beckerle, Philipp Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article In: Advanced Intelligent Systems, pp. 1–28, 2021. @article{Cansev2021, | ![]() |
2020 |
|
Rottmann, N.; Bruder, R.; Xue, H.; Schweikard, A.; Rueckert, E. Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020. @inproceedings{Rottmann2020c, | ![]() |
Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E. Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. @inproceedings{Xue2020, | ![]() |