Honghu Xue, M.Sc.

Ph.D. Student at the University of Luebeck

Portrait of Honghu Xue

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
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Publcations

2021

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

Advanced Intelligent Systems, pp. 1–28, 2021.

Links | BibTeX

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience

2020

Rottmann, N; Bruder, R; Xue, H; Schweikard, A; Rueckert, E

Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings

Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020.

Links | BibTeX

Parameter Optimization for Loop Closure Detection in Closed Environments

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

International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks