Opening of the exibition: Friday, May 19, 2017, 7:00 p.m., Schloß Hohentübingen (free entrance)
The exhibition in the Museum Ancient Cultures (Hohentübingen Castle) will focus on the most important steps of humankind. Our institute supports the last part of the exibition "Origin of digital innovation" with a Nao robot and the Mosh Camera App.
The Heinz Maier-Leibnitz Prize is recognized as the most important science award in Germany to early career researchers. It was awarded May 3rd, 2017 in Berlin.
Hosted this time by Jeannette Bohg
Science, not Silence!
The Managing Directors encourage staff and supporters of the Max Planck Institute for Intelligent Systems to participate in March for Science events.
Call for Applications - Ph.D. positions
The International Max Planck Research School (IMPRS) for Intelligent Systems (IS) is starting in fall 2017. This new doctoral program will enroll about 100 Ph.D. students over the next six years. Apply now!
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.
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.
Guest edited by 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.