About me
3nd-year PhD student in Computer Science, University of Hamburg, Knowledge Technology (WTM) group, advised by Prof. Stefan Wermter.
M.Sc in Computer Science and Technology, University of Electronic Science and Technology of China, advised by Prof. William Zhu, 2018-2021.
B.E. in Spatial Information and Digital Technology, University of Electronic Science and Technology of China, advised by Prof. Fen Chen, 2014-2018.
I am deeply interested in the critical methodologies driving Embodied Intelligence, particularly Reinforcement Learning, Large Language Models (LLMs), and Robotics. In humanoid robot development, three key components shape their capabilities: the Brain, the Cerebellum, and the Body. My current research focuses on two of these aspects:
Brain – Leveraging LLMs for planning in long-horizon bimanual robotic tasks.
Cerebellum – Developing bimanual manipulation skills using foundation models.
Through my work, I aim to bridge high-level reasoning and planning with low-level motion control, pushing the boundaries of intelligent robotic systems.
The Future of Embodied Intelligence is Now.
What’s New
[2025.04] Our paper, LLM-iTeach: LLM-based Interactive Imitation Learning for Robotic Manipulation, has been accepted by IJCNN 2025. Congrations to Jonas!
[2025.03] Our paper, LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language, is now available online on arXiv. Please check the video as well on Youtube!
[2024.10] We have open-sourced the code for the LABOR Agent on Github.
[2024.09] Our paper Large Language Models for Orchestrating Bimanual Robots has been accepted by Humanoids 2024. See you in Nancy, France in November!
[2024.07] Our application to Researcher Access Program of OpenAI has been accepted and we have been awarded 5000 USD in API credits!
[2024.03] The project website of LABOR Agent: Large Language Models for Orchestrating Bimanual Robots is released! Paper in arxiv version can be found at here.
[2024.02] I am glad to be served as technical committee member of the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots.
[2024.02] Our paper Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic has been accepted at COLING 2024.
[2023.11] Our paper Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models has been accepted as oral presentation at the 7th Conference on Robot Learning (CoRL 2023) Workshop on Bridging the Gap between Cognitive Science and Robot Learning in the Real World: Progresses and New Directions.
Glad to discuss anything! Please contact me at kun.chu at uni-hamburg dot de