Jun Jin, PhD

Area of Study / Keywords

Robotics Reinforcement Learning Continual RL Embodied AI Open-Ended Agent Learning

About

I am an assistant professor in the ECE department at the University of Alberta and a Fellow of Amii. 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.

My work in real-world robotic reinforcement learning received the Outstanding Student Paper Award Honorable Mention in ICRA 2022 (the flagship conference of IEEE Robotics and Automation Society) and the KUKA Innovation Award 2018 Global top-5. Before joining the University of Alberta, I gained eight years of professional experience in autonomous driving, autonomous open-pit mining, and construction automation. I love hiking and solo camping in the Rocky Mountains.

Website: https://www.ece.ualberta.ca/~jjin5/

Research

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

Application: Healthcare Robotics

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.

My long-term goal is to develop scalable learning architectures and algorithms to make robots 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. Visit my website for more details.

Teaching

Starting fall term 2026, I will offer a new course: ECE 562 - Deep Reinforcement Learning for Robotics

ECE 562 will use the full semester to explore deep reinforcement learning (RL) algorithms. This course is good for students to learn machine learning (ML) basics and RL basics within a single semester, while gaining exposure to a broad range of deep RL topics, including value-based and policy gradient methods, actor–critic algorithms, model-based RL, inverse RL, temporal abstraction methods, options for skill discovery, and real-world applications of RL in robotics.

Past courses I taught:

Started in 2024, I taught ECE 720 B01 Robot Learning: Principles and Advances, every winter term. This course covers topics in robot learning (60% reinforcement learning, 20% continual learning, 20% embodied AI).

Note: ECE720 B01, and ECE720 A02 Robot Learning: Principles and Advances will not be offered until further notice.

I love working with students! I am looking for highly motivated students to join my group, the Human-Centered Autonomy Lab. Read here if you expect a strong reply from me. Students in my group will be able to join the Amii student intra-network, which provides abundant resources regarding funding, computing, and industrial connections.

I welcome undergraduate students who are currently at the UofA and are interested in joining a research project in my lab. But research requires essential skills. Please firstly join our reading club regularly which will help you prepare the background skills needed. 

Due to the massive inquiries I received each week, I cannot respond in a timely manner. Please be prepared that it's relatively very competitive to get an RA position in my lab.

ENCMP 100 - Computer Programming for Engineers

Fundamentals of computer programming with emphasis on solving engineering problems. Structure and syntax of computer programs, variables, data types, data structures, control structures, functions, input/output operations, debugging, software development process.

Browse more courses taught by Jun Jin

• Google Scholar
• Lab Website