Neues aus dem Institut

Die Tübinger Machine Learning Summer School ist der Hit

  • 19 June 2017

Die Machine Learning Summer School findet zum fünften Mal in Tübingen statt und erfreut sich steigender Nachfrage


Awards for Two Master Theses at the Autonomous Motion Department

  • 07 June 2017

Cédric de Crousaz and Julian Viereck receive the ETH Medal for their outstanding Master Theses

Sebastian Trimpe Ludovic Righetti Julian Viereck


Finalist for the Best Robotic Vision Paper

  • 01 June 2017

at the 2017 IEEE/RAS International Conference on Robotics and Automation

The paper "Probabilistic Articulated Real-Time Tracking for Robot Manipulation" by Cristina Garcia Cifuentes, Jan Issac, Manuel Wüthrich, Stefan Schaal and Jeannette Bohg was finalist for the Best Robotic Vision paper at the 2017 IEEE/RAS International Conference on Robotics and Automation.

Cristina Garcia Cifuentes Manuel Wüthrich Jan Issac Stefan Schaal Jeannette Bohg


Release of Bayesian Articulated Object Tracking Libraries

  • 29 May 2017

Robust and real-time Bayesian articulated object tracking methods, implemented in C++ and CUDA.

We release open-source code and data sets on Bayesian articulated object tracking. The library contains approaches towards problems ranging from single object tracking to full robot arm pose estimation. The data sets allow the quantitative evaluation of alternative approaches thanks to accurate ground-truth annotations.

Cristina Garcia Cifuentes Jan Issac Manuel Wüthrich Jeannette Bohg



Heinz Maier-Leibnitz-Preis 2017 für Andreas Geiger

  • 03 May 2017

Der wichtigste Preis für den wissenschaftlichen Nachwuchs in Deutschland ist am 3. Mai 2017 in Berlin verliehen worden.

Andreas Geiger


Local networking event for female researchers from Tübingen

  • 28 April 2017

Hosted this time by Jeannette Bohg

Jeannette Bohg


DOOMED - A new online learning approach from AMD in the spotlight

  • 01 March 2017

Text: Kathryn Ryan. New Rochelle, February 21, 2017.

Robotics researchers have developed a novel adaptive control approach based on online learning that allows for the correction of dynamics errors in real time using the data stream from the robot. The strategy is described in an article published in Big Data, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website until March 14, 2017.

Franzi Meier Daniel Kappler Nathan Ratliff Stefan Schaal


How Can We Use Machine Learning in the Search for Exoplanets?

  • 01 February 2017

Bernhard Schölkopf joined the initiative "Latest Thinking"

Exoplanets are planets beyond our own solar system. Since they do not emit much light and moreover are very close to their parent stars they are difficult to detect directly. When searching for exoplanets, astronomers use telescopes to monitor the brightness of the parent star under investigation: Changes in brightness can point to a passing planet that obstructs part of the star’s surface. The recorded signal, however, contains not only the physical signal of the star but also systematic errors caused by the instrument. As Bernhard Schölkopf explains in this video, this noise can be removed by comparing the signal of the star of interest to those of a large number of other stars. Commonalities in their signals might be due to confounding effects of the instrument. Using machine learning, these observations can be used to train a system to predict the errors and correct the light curves.

Bernhard Schölkopf


Big Data in Robotics

  • 02 January 2017

Als Gast Editoren: Jeannette Bohg, Matei Ciocarlie, Javier Civera, Lydia E. Kavraki.

"... new big data methods have the potential to allow robots to understand and operate in significantly more complex environments than was possible even in the recent past. This should lead to a qualitative leap in the performance and deployability of robotics in a wide array of practical applications and real settings."

Jeannette Bohg