Jens Lundell

Jens Lundell

I started as a PhD. student at the MR&O Lab at the Center for Applied Autonomous Sensors Systems (AASS) in October 2016. Previously I have been affiliated with the intelligent robotics group at Aalto University. My main research focus will be on robotic manipulation, trying to incorporate both optimal control and learning for both motion planning and grasping.

Brief CV

I was born in 1991 in a small town named Närpes, Finland. After graduating high school I moved to Espoo to study at Aalto University. I received my B.Sc. in Automation and Systems Technology (2013). After this I went on to an Erasmus Mundus program in Space Science and Technology (SpaceMaster). The SpaceMaster program was a joint collaboration with several universities in Europe including Aalto University. The first year in the SpaceMaster program I studied one semester at the University of Würzburg in Germany and one at Luleå University of Technology in Sweden. For the second year I returned to Aalto University where I also received my M.Sc. in Space Science and Robotics (2016). After this I continued working for a short term at Aalto University before I joined AASS as a PhD. student in October 2016.

Research Interests

My main research interests are in robotic manipulation, reinforcement learning (RL), and learning from demonstrations. In my master’s thesis I taught a robot how to play the Ball-in-a-Cup game by initially modeling the skill as a dynamic movement primitive and then fine-tuning it with subsequent RL.

Contact

Jens Lundell

AASS Research Centre
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T1227
Phone +46 (0)19 30 35 91
jens.lundell@oru.se

Publications

Refereed Conference and Workshop Articles

[1] J. Lundell, R. Krug, E. Schaffernicht, T. Stoyanov and V. Kyrki. Safe-To-Explore State Spaces : Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization. In IEEE-RAS Conference on Humanoid Robots, pages 132-138, 2018BibTeX | DiVA  ]

Theses

[1] J. Lundell. Dynamic movement primitives and reinforcement learning for adapting a learned skill. M.Sc. Thesis, 2016-08-24BibTeX | PDF ]

Find the complete BibTeX record here.

 
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