"Probabilistic Articulated Real-Time Tracking for Robot Manipulation"
The paper has been published in IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 577-584, April 2017. It has been considered the best paper published on IEEE Robotics and Automation Letters in 2017. IEEE RA-L published 305 papers in 2017.
This paper is among the 5 best of about 2500 submissions. The 2018 International Conference on Robotics and Automation (ICRA) will take place from 21 until 25 May 2018 at the Brisbane Convention & Exhibition Centre, Brisbane, Australia.
Robohub 2017 list of 25 women in Robotics you need to know about
On the occasion of Ada Lovelace Day on 10 October 2017, robohub presented their annual list of “25 women in robotics you need to know about”. Recently, Jeannette Bohg became Assistant Professor in Computer Science at Stanford. She is Guest Researcher at the Autonomous Motion Department of MPI, where she did her research on robotics between 2012 and 2017. Congratulations!
Cédric de Crousaz and Julian Viereck receive the ETH Medal for their outstanding Master Theses
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.
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.
Hosted this time by Jeannette Bohg
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.
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.