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Jun Jin
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Curriculum Vitae

Jun Jin

Assistant Professor, Electrical & Computer Engineering Dept
University of Alberta
Fellow, Alberta Machine Intelligence Institute (Amii)
Founder and Director, Human-Centered Autonomy Lab (HCA Lab)

Email: email address
Office: 11-365 Donadeo ICE building, 9211-116 St, Edmonton, AB, T6G 2H5

Research Interests:
Robotics  ·  Reinforcement Learning  ·  Continual RL  ·  Embodied AI  ·  Open-Ended Agent Learning

Application: Healthcare Robotics

I investigate how intelligent machines can learn and adapt continuously through real-world interactions. I focus on the intersection of reinforcement learning (RL) and embodied artificial intelligence (EAI), studying how robots acquire, refine, and reuse motor skills through direct interaction with their physical environment.
As AI systems move into open-ended real-world environments, a key challenge is enabling learning that scales through experience rather than data or model size alone. I am interested in developing computational models that allow robots to build predictive internal representations of their own actions and outcomes instead of merely static predictions, enabling lifelong learning agents that operate robustly across changing tasks, environments, and human needs. This direction supports the development of robots as general-purpose tools — systems that learn over time, collaborate with people, and remain adaptable in big world settings – an ideal form of “human-centered autonomy”, which is my vision for AI/Robotics research.

Latest Updates

🔴 Recruiting: I am actively recruiting several highly motivated PhD and MSc students (with a strong preference for PhD candidates). Thanks to a recent major funding, we are in an excellent position to support multiple new students. Graduate students will be fully funded. Postdoctoral researchers with their own external funding are also very welcome to reach out.

Note: Due to the extraordinarily high interest and rapid trend in our lab's focused research topics, admission to our lab is highly competitive. Please check our lab posting for detailed requirements before reaching out.

Lab Members

Lab Students Group Photo

Graduate Students

PhD Students

MSc Students

Undergraduate Students

High School Students

Research Roadmap

Jun Jin's 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 fascinated by the idea of building an agent model that is embodied and embedded in the environment. This means internally, the agent learns extendable embodied skills from its past experience, and externally, the agent model has interfaces to enable it to be embedded in the environment.

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. These hardware platforms are central to our research plan, which focuses on three interconnected directions:

  1. Robotics: Using mobile manipulators and dexterous hands, we study how robots can adaptively coordinate and reconfigure themselves to handle complex tasks.
  2. Neural Architectures and Learning Algorithms: We design learning models that can acquire single-agent skills, develop multi-agent collaboration, and adapt in continuously changing environments.
  3. Human–Robot Interaction: Exploring how robots follow human commands and how they can be easily reprogrammed as general-purpose tools by users for novel tasks.

Research Philosophy

1. Scientific discovery: A revised "Bitter Lesson" for robotics

I am a believer in most parts of "The Bitter Lesson" by Dr. Rich Sutton, but robotics research has its own characteristics: (1) Data is more costly, and (2) Human is an important factor. Therefore, finding first-principle learning priors in a data-driven approach becomes critical.

Revised bitter lesson for robotics

2. Social impact: A broader view beyond full-autonomy

Tavakoli et al. 2020 provides a holistic view of robotic applications categorized by how much task uncertainty the robot deals with. The social impact of robotics can be much broader than research that only considers full-autonomy. I propose moving from strictly full-autonomy to human-centered autonomy.

Broad view of robotic applications

HCA Reading Club

Reading Club Event

Our HCA Lab organizes a weekly reading group. We focus on presenting tutorials for machine learning/robotics beginners and sharing frontier robotics research papers. We meet virtually every Monday 11:00 am - 12:00 pm. Contact me if you have an interest.

Last updated: 2026
© Dr. Jun Jin. CC BY 4.0.