Reactive stepping is an important utility to regain balance when bipedal walking motions are disturbed. This paper sheds light on the reasons for humanoid robots to fall down. It presents a method to calculate modifications of predefined foot placements with the objective to minimize deviations of the Zero Moment Point from a reference without interrupting the walk. The calculation is in closed-form, and is embedded into a well-evaluated preview controller with observer based on the 3D Linear Inverted Pendulum Mode (3D-LIPM). Experiments in simulation and on a physical robot prove the benefit of the proposed system.

This paper presents a vision system which is designed to be used by the research community in the Standard Platform League ( http://www.tzi.de/spl ) (SPL) and potentially in the Humanoid League ( http://www.robocuphumanoid.org ) (HL) of the RoboCup. It is real-time capable, robust towards lighting changes and designed to minimize calibration. We describe the structure of the processor along with major ideas behind object recognition. Moreover, we prove the benefit of the proposed system by assessing recorded image data on the robot hardware. The vision system has already been successfully employed with the NAO robot by Aldebaran Robotics ( http://www.aldebaran-robotics.com ) in prior RoboCup competitions as well as several minor events.

Predicting a CT image or a map of the linear attenuation coefficients from the information provided by magnetic resonance imaging (MRI) is a challenging task. This problem is of significant importance for combined positron emission tomography (PET)/MRI scanners, as quantitative PET image reconstruction requires an attenuation map. In PET/CT this attenuation map is derived from the CT scan or from a rotating source, however, current PET/MR systems can not directly measure attenuation images - and indeed it is desirable to save the patient from the additional radiation exposure. Recent approaches tackle this problem by using MR sequences with ultra-short echo times (UTE). At the price of lower effective image resolution, the UTE image yields signal from bone and therefore provides valuable information for calculating the attenuation map. We propose a novel approach to this problem based on nonnegative blind deconvolution and present the first method that explicitly models the image degradation of the UTE image. Incorporating prior knowledge such as smoothness and a novel orthogonality constraint alleviates the deconvolution process. Due to its probabilistic formulation our approach allows hyperparameter estimation and is therefore parameter-free.

Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and exploits correlations between neighboring pixels in the reference and the floating image. Empirical evaluation on CT-MR/PET-MR rigid registration tasks demonstrates that our approach yields robust performance and outperforms the state of the art methods for multi-modal medical image registration.