My name is Johannes A. Stork and I am a post-doctoral researcher at the Royal Institute of Technology (KTH), Sweden. I work at the Robotics, Perception, and Learning Lab (RPL) and the Center for Autonomous Systems (CAS).
My main research interest is in autonomous intelligent systems. In specific I am interested in how agents can make decision under uncertainty and learn from experience in dynamic and human-populated environments. I have been working on a large range of different applications: robotic grasp design, in-hand manipulation, and topology-based caging, as well as, tracking of pedestrians, socially normative and visual robot navigation, and further audio-based activity recognition and structured prediction for scene understanding. I am also interested in Bayesian statistics and reinforcement learning.
Short Bio: Before joining RPL as a post-doctoral researcher, I was a PhD. student in Computer Science (Computer Vision and Robotics) at the Computer Vision and Active Perception Lab (CVAP) at the Royal Institute of Technology (KTH) in Stockholm. I did my graduate research as a member of Professor Danica Kragic’s research group where my co-supervisors were Carl Henrik Ek and Yasemin Bekiroglu. Before that, I spent several years of my undergraduate studies at the University of Freiburg, Germany, as a student research assistant at the Social Robotics Lab of Professor Kai O. Arras. I hold a MSc. and a BSc. degree in Computer Science with concentration in Artificial Intelligence and Robotics with minor in Mathematics from Freiburg University.
Our new paper about non-parametric spatial context structure learning was accepted to RO-MAN 2017. (Thippur, A., Stork, J. A., & Jensfelt, P. (2017). Non-Parametric Spatial Context Structure Learning for Autonomous Understanding of Human Environments. In Proc. IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’17).)
Our paper was selected for oral presentation at ICRA 2017. (Hang, K., Stork, J. A., Pollard, N. S., & Kragic, D. (2017). A Framework For Optimal Grasp Contact Planning. IEEE Robotics and Automation Letters, 2(2), 704–711.)
Our new paper about design of optimal robotic grasps was accepted to Robotics and Automation Letters. (Hang, K., Stork, J. A., Pollard, N. S., & Kragic, D. (2017). A Framework For Optimal Grasp Contact Planning. IEEE Robotics and Automation Letters, 2(2), 704–711.)
I started as a post-doctoral researcher at KTH.