Rate My Professor Joni Pajarinen

Joni Pajarinen is an Associate Professor in the Department of Electrical Engineering and Automation at Aalto University, leading the Robot Learning research group. He earned his Master of Science in Technology and Doctor of Science in Engineering and Technology from Aalto University, with his doctoral degree awarded on 13 February 2013. His dissertation, titled Planning under Uncertainty for Large-Scale Problems with Applications to Wireless Networking, addressed decision-making challenges in uncertain environments. Following his doctorate, Pajarinen conducted postdoctoral research in the Intelligent Robotics group at Aalto University from 2012 to 2015 and served as a visiting researcher at Carnegie Mellon University in 2014. He later became Research Group Leader at the Intelligent Autonomous Systems lab at TU Darmstadt before returning to Aalto University as Assistant Professor and advancing to Associate Professor.

Pajarinen's research specializes in robot learning, multi-agent reinforcement learning, imitation learning, mobile manipulation, decision making under partial observability, and autonomous systems. His work enables robots to learn independently, operate in unknown environments, and assist humans proactively, with applications in heavy machinery, underwater vehicles, and humanoid robots. As principal investigator, he directs projects including ENGAGE for training engineers in mobile working machines, XSCAVE for explainable safe control in heavy machinery, Aurora, IWM/Tuomisto/T41020, and MARL for efficient multi-agent reinforcement learning. Key publications encompass An Algorithmic Perspective on Imitation Learning (2018, Foundations and Trends in Robotics), Partially Observable Markov Decision Processes in Robotics: A Survey (2022, IEEE Transactions on Robotics), Self-Paced Deep Reinforcement Learning (2020, NeurIPS), Robotic Manipulation of Multiple Objects as a POMDP (2017, Artificial Intelligence), Curriculum Reinforcement Learning via Constrained Optimal Transport (2022, ICML), and AgentMixer: Multi-Agent Correlated Policy Factorization (2025, AAAI). With 85 research outputs, including 45 conference papers and 25 journal articles, Pajarinen advances AI-driven sustainability, productivity, and safety in robotics. He has presented at conferences such as ICML 2008 and MLSP 2008 and held positions of trust, including as Chair at a university external organization in 2011.