Honghu Xue, M.Sc.

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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.

Research Videos

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

2022

Herzog, Rebecca; Berger, Till M; Pauly, Martje Gesine; Xue, Honghu; Rueckert, Elmar; Munchau, Alexander; B"aumer, Tobias; Weissbach, Anne

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques Journal Article

In: Frontiers in Neuroscience, 2022.

Links | BibTeX

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques

Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics Journal Article

In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022, (Supplement: https://cloud.cps.unileoben.ac.at/index.php/s/Sj68rQewnkf4ppZ).

Abstract | Links | BibTeX

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

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.

Abstract | Links | BibTeX

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation

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.

Links | BibTeX

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

2020

Akbulut, M Tuluhan; Oztop, Erhan; Seker, M Yunus; Xue, Honghu; Tekden, Ahmet E; Ugur, Emre

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing Conference

2020.

Abstract | Links | BibTeX

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing

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.

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

In: 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