Emil Hyttinen

I was previously an PhD student at the Robotics, Perception and Learning Lab (RPL) at KTH. My research topics was machine learning, robotic grasping, manipulation and tactile sensing. Large parts of my research was carried out at ULg in Belgium. I defended my licentiate thesis in July 2017. My supervisors were Prof. Danica Kragic and Dr. Renaud Detry. In my free time I enjoy practicing orienteering and playing board games. I also have a new found hobby in competitive programming.

Conference publications:

These publications are copyrighted by their respective publishers. Downloadable versions are not necessarily identical to the published versions. They are made available here for personal use only.

E. Hyttinen, D. Kragic and R. Detry, Learning the Tactile Signatures of Prototypical Object Parts for Robust Part-based Grasping of Novel Objects. In IEEE International Conference on Robotics and Automation, 2015.

pdf ; show bibtex

@inproceedings{hyttinen2015a,
Author = {Emil Hyttinen and Danica Kragic and Renaud Detry},
Booktitle = {{IEEE} International Conference on Robotics and Automation},
Title = {Learning the Tactile Signatures of Prototypical Object Parts for Robust Part-based Grasping of Novel Objects},
Year = {2015}}	

E. Hyttinen, D. Kragic and R. Detry, Estimating Tactile Data for Adaptive Grasping of Novel Objects. In IEEE RAS International Conference on Humanoid Robots, 2017.

pdf ; show bibtex

@inproceedings{hyttinen2017a,
Author = {Emil Hyttinen and Danica Kragic and Renaud Detry},
Booktitle = {{IEEE} International Conference on Humanoid Robots},
Title = {Estimating Tactile Data for Adaptive Grasping of Novel Objects},
Year = {2017}}	

Theses:

E. Hyttinen, Adaptive Grasping Using Tactile Sensing. Licentiate Thesis, KTH 2017.

pdf ; show bibtex

@misc{lic-thesis-Hyttinen,
Author = {Emil Hyttinen},
Title = {Adaptive Grasping Using Tactile Sensing},
Howpublished = {Licentiate's Thesis, KTH},
Year = {2017}}	

E. Hyttinen, Learning probabilistic models for the topology of unexplored parts of indoor environments using a floor plan dataset. Master Thesis, KTH 2017.

pdf ; show bibtex

@mastersthesis{msc-thesis-Hyttinen,
Author = {Emil Hyttinen},
Title = {Learning probabilistic models for the topology of unexplored parts of indoor environments using a floor plan dataset},
School = {KTH},
Year = {2017}}	

E. Hyttinen, Simon Nee, Simon Ye Fundamental limits to force detection using a bulk mode resonator in liquid. Bachelor Thesis, KTH 2011.

pdf ; show bibtex

@misc{bsc-thesis-Hyttinen,
Author = {Emil Hyttinen and Simon Nee and Simon Ye},
Title = {Fundamental limits to force detection using a bulk mode resonator in liquid},
Howpublished = {Bachelor's Thesis, KTH},
Year = {2011}}	

Contact:
Tel: +46702613515
E-post: emilhy (at) kth.se