Dynamic Motion Modelling for Legged Robots

An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effort. In this paper we present a motion model representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the need to manually design the form of a motion model, and provides a direct means of incorporating auxiliary sensory data into the model. This representation and its accompanying algorithms are validated experimentally using an 8-legged kinematically complex robot, as well as a standard benchmark dataset. The presented method not only learns the robot's motion model, but also improves the model's accuracy by incorporating information about the terrain surrounding the robot

Structural investigations of CeIrIn5{_5} and CeCoIn5{_5} on macroscopic and atomic length scales

For any thorough investigation of complex physical properties, as encountered in strongly correlated electron systems, not only single crystals of highest quality but also a detailed knowledge of the structural properties of the material are pivotal prerequisites. Here, we combine physical and chemical investigations on the prototypical heavy fermion superconductors CeIrIn${_5}$ and CeCoIn${_5}$ on atomic and macroscopic length scale to gain insight into their precise structural properties. Our approach spans from enhanced resolution X-ray diffraction experiments to atomic resolution by means of Scanning Tunneling Microscopy (STM) and reveal a certain type of local features (coexistence of minority and majority structural patterns) in the tetragonal HoCoGa$_5$-type structure of both compounds.Comment: 8 pages, 5 figures, submitted to JPSJ (SCES 2013