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Curriculum Vitae
Xiangtong Yao is currently a Ph.D. student in the Chair of Robotics, AI and Real-time Systems. His research advisor is Prof. Alois Knoll. He received his M.Eng degree in School of Computer Science and Engineering at Sun Yat-sen University, China. Before he come to TUM to pursue his doctoral degree, he worked at Sun Yat-sen University as a research assistant. His research interests include Language-conditioned Meta-Reinforcement Learning for Manipulation tasks and Human-robot Interaction with New Tactile Sensor, such as triboelectric nanogenerator (TENG).
Thesis Topic
Topic 1: Language-conditioned Meta-Reinforcement Learning for Manipaulation Tasks
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multiple-task scenarios due to the insufficient task information provided only by rewards. Inspired by teaching processing, Language-conditioned Meta-RL improves the generalization by matching language instructions and the agent’s behaviors. In this topic, our goal is combining NLP technologies with Meta-RL for manipulation tasks to enable robot to solve multiple complex manipulation tasks. For example, combining synonyms and antonyms of language instructions with robot's behaviors can improve the learning efficiency and generalization of robot in solving symmetrical tasks. For more information, please visit [2209.10656v1] Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions (arxiv.org)
1: Language as abstractions for Meta-reinforcement learning
2: Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions
If you are interested in the above topics, please feel free to contact me indicating your background and skills.
Demo
Video of Meta-Reinforcement learning with symmetric data and language instructions
Bing, Zhenshan; Koch, Alexander; Yao, Xiangtong; Huang, Kai; Knoll, Alois: Meta-Reinforcement Learning via Language Instructions. 2023 IEEE International Conference on Robotics and Automation(ICRA), 2023 more…
Wang, Mingyang; Bing, Zhenshan; Yao, Xiangtong; Wang, Shuai; Huang, Kai; Su, Hang; Yang, Chenguang; Knoll, Alois: Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning. Association for the Advancement of Artificial Intelligence, AAAI, 2023 more…
Yao, Xiangtong; Bing, Zhenshan; Zhuang, Genghang; Chen, Kejia; Zhou, Hongkuan; Huang, Kai; Knoll, Alois: Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), 2023 more…
Zhuang, Genghang; Bing, Zhenshan; Yao, Xiangtong; Huang, Yuhong; Huang, Kai; Knoll, Alois: An Energy-Efficient Lane-Keeping System Using 3D LiDAR Based on Spiking Neural Network. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), 2023 more…
Zhuang, Genghang; Bing, Zhenshan; Yao, Xiangtong; Huang, Yuhong; Huang, Kai; Knoll, Alois: Toward Intelligent Sensing: Optimizing Lidar Beam Distribution for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 24 (8), 2023, 8386-8392 more…
2021
Shan, Yunxiao; Yao, Xiangtong; Lin, Hongquan; Zou, Xuesong; Huang, Kai: Lidar-Based Stable Navigable Region Detection for Unmanned Surface Vehicles. IEEE Transactions on Instrumentation and Measurement 70, 2021, 1-13 more…
2019
Chen, Longsheng; Chen, Yuanpeng; Yao, Xiangtong; Shan, Yunxiao; Chen, Long: An Adaptive Path Tracking Controller Based on Reinforcement Learning with Urban Driving Application. 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019 more…
Yao, Xiangtong; Shan, Yunxiao; Li, Jieling; Ma, Donghui; Huang, Kai: LiDAR Based Navigable Region Detection for Unmanned Surface Vehicles. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2019 more…
