Yuchen Xiao
PhD Student
Education
- MS, Columbia University in the City of New York
- MEng, Dalian University of Technology — China
- BS, Kunming University of Science and Technology — China
Biography
Yuchen Xiao is a PhD student studying robotics at Northeastern University’s Khoury College of Computer Sciences, advised by Professor Christopher Amato. Yuchen’s research is focused on multi-agent/robot hierarchical decision-making under partial observability. Besides, Yuchen also has contributions to general multi-agent reinforcement learning and online planning for robotics manipulation under uncertainty. Prior to joining the PhD program, Yuchen earned his Master of Science from Columbia University, his Master of Engineering from Dalian University of Technology in China, and his Bachelor of Science from Kunming University of Science and Technology in China.
What are the specifics of your graduate education (thus far)?
Currently, I am pursuing a PhD degree in the Khoury College with a concentration on robotics decision making under uncertainty advised by Professor Christopher Amato.
What are your research interests?
My research interests are to apply reinforcement learning, online planning, and optimization techniques to create more efficient multi-robot systems.
What’s one problem you’d like to solve with your research/work?
I’d like to develop a more reliable and effective decision-making algorithm for the multi-robot system to enable the robots to quickly and collaboratively to search for target objects in a cluttered environment for either a human or the robot's teammates to achieve a high-level task.
What aspect of what you do is most interesting?
Currently, as robots are becoming more and more intelligent, they’ll start to enter our working and life environments socially, performing human tasks. Robots cooperating with each other and collaborating with humans will become the next frontier.
What are your research or career goals, going forward?
I would like become an expert in the robotics field and a professional in both the hardware and the software of robotics.
Where did you grow up or spend your most defining years?
Shenyang, in the northeast of China.
Recent Publications
-
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Citation: Lyu, Xueguang et al. “Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning.” AAMAS (2021). -
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
Citation: Xiao, Y., Hoffman, J. & Amato, C.. (2020). Macro-Action-Based Deep Multi-Agent Reinforcement Learning. Proceedings of the Conference on Robot Learning, in Proceedings of Machine Learning Research 1 -
Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions
Citation: Xiao, Y., Hoffman, J., Xia, T., & Amato, C. (2020). Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13965-13966.