Jun Jin
I am an assistant professor in the ECE department at the University of Alberta and a Fellow of Alberta Machine Intelligence Institute (Amii). Generally, I am interested in Robotics and Machine Learning research. Specifically, I focus on topics in robotic reinforcement learning which intersects with embodied artificial intelligence, the theory of predictive coding, continual learning and open-ended learning agents.
Email: 
Office: 11-365 Donadeo ICE building, 9211-116 St, Edmonton, AB, T6G 2H5
News
- Our group is hiring! I am not accepting new students at the moment. Thank you very much for your interest! If you already have external funding (e.g., NSERC), please feel free to reach out. If you are highly motivated to pursue a PhD with me—particularly in line with the research roadmap outlined below—you are also welcome to contact me even if you do not yet hold a scholarship. Check our lab posting for any updates..
- September 2025: Our lab will be receiving two mobile bi-manipulators and one dexterous robotic hand, made possible through the generous support of CFI and the Government of Alberta.
- August 2025: Our lab has received major funding from the Canada Foundation for Innovation (CFI) and the Government of Alberta for the project “Research Infrastructure for Human-level Robotic Manipulation Task Mastering.” This generous support will be used to procure advanced robotic hardware, sensors, and system integration work.
- July, 2025: Dr. Jun Jin delivered a tutorial on "Embodied AI" at CIFAR DLRL summer school.
- June, 2025: Our group presented in the Northwest Robotics Symposium.
- May, 2025: Dr. Jun Jin gave a talk at the Amii Upper Bound 2025 about "Demistifying Embodied AI from the perspective of Robot Motion Generation".
Students
Graduate Students
- Dalen Shi (PhD student)
- Yi Hu (PhD student)
- Shelton Ge (PhD student, co-supervised with Prof. Ming Lu (main supervisor))
- Arya Ebrahimi (MSc student)
- Daxton Dion-Hoffman (MSc student)
- Qianxi Li (previous MSc student, co-supervised with Prof. Matthew Taylor (main supervisor))
Undergraduate Students
- Landys Lepine (4th year Computing Science student, worked as summer intern in 2025.)
My research roadmap
My long-term goal is to discover first-principle learning priors to develop scalable learning architectures and algorithms to make robots (agents) agile to move, easier to program,
and human-aware to interact with, which in all compose my vision for the future of Robotics and Artificial Intelligence: Human-Centered Autonomy. I am mostly facinated
by the idea of building an agent model that is embodied and embedded in the environment, which means internally, the agent learns
extendable emobodied skills from its past experience, and externally, the agent model has interface to enable it embedded in the environment.
In a layman's languange, I aim to build an agent model
that can easily adapt its learned skills to new tasks and new environmental dynamics, that can be easily re-programmed by human, and that that is human-aware to interact with. Check more details of this research roadmap here.
Robots in Our Lab
We received our first robot, the Franka Research 3, in September 2024. More robots—including mobile bi-manipulators and dexterous robotic hands—will be joining the lab in 2025 thanks to the generous support of CFI and the Government of Alberta.
These hardware platforms are central to our research plan, which focuses on three interconnected directions:
1. Robotic Morphology: Using mobile manipulators and dexterous hands, we study how robots can adaptively coordinate and reconfigure themselves—for example, forming bi-manipulators or tri-manipulators to handle complex tasks.
2. Neural Architectures and Learning Algorithms: With these robots as physical testbeds, we design learning models that can acquire single-agent skills, develop multi-agent collaboration, and adapt in continuously changing environments through lifelong learning.
3. Human–Robot Interaction: The new hardware also enables us to explore how robots folllow human commands and how they can be easily reprogrammed as general-purpose tools by users for novel tasks, bridging advanced AI with practical usability.
My research philosophy
(1) Scientific discovery: a revised "Bitter Lesson" for robotics.
I am a believer in most parts of "The Bitter Lesson" written by Dr. Rich Sutton, but robotics research has its own characteristics
that are different from a general agent definition like in Atari games, which are (1) Data is more costly, and (2) Human is an important factor! I believe we should bake in
some "smart" learning architectures and algorithms in somewhere middle, as depicted in the diagram below, to address the two challenges. Therefore, finding first-principle learning priors in a data-driven approach becomes important!
(2) Social impact: take a BROADER view, it's not only full-autonomy!
Tavakoli et al. 2020 provide a holistic view of robotic applications categorized by how much task uncertainty that the robot deals with.
It clearly shows that the social impact of robotics can be much broader than researches only consider full-autonomy, which is what most of our peer researchers in robot learning are working on.
I propose to take a broader view, to move from
only full-autonomy to human-centered autonomy, which will help us shape the future of robotics research towards building a world with robots by human side, providing every help as best depicted in the ICRA 2015 poster above.
HCA Reading Club - Share, Learn, and Improve
Our HCA Lab organizes a weekly reading group. We focus on both presenting tutorials for machine learning/robotics beginners, and sharing frontier robotics research papers.
We meet virtually every Mon 11am~12pm except for holidays for the fall term 2024 schedule. Everyone is welcome to join. Contact 
if you have an interest.
