Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks

A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.Comment: 7 pages, 8 figure

Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates

Neumann K, Lemme A, Steil JJ. Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates. Presented at the Int. Conference Intelligent Robotics and Systems, Tokio

OOP: Object-Oriented-Priority for Motion Saliency Maps

Belardinelli A, Schneider WX, Steil JJ. OOP: Object-Oriented-Priority for Motion Saliency Maps. In: Workshop on Brain Inspired Cognitive Systems. 2010: 370-381

New representation of water activity based on a single solute specific constant to parameterize the hygroscopic growth of aerosols in atmospheric models

Water activity is a key factor in aerosol thermodynamics and hygroscopic growth. We introduce a new representation of water activity (<i>a</i><sub>w</sub>), which is empirically related to the solute molality (μ<sub>s</sub>) through a single solute specific constant, ν<sub><i>i</i></sub>. Our approach is widely applicable, considers the Kelvin effect and covers ideal solutions at high relative humidity (RH), including cloud condensation nuclei (CCN) activation. It also encompasses concentrated solutions with high ionic strength at low RH such as the relative humidity of deliquescence (RHD). The constant ν<sub><i>i</i></sub> can thus be used to parameterize the aerosol hygroscopic growth over a wide range of particle sizes, from nanometer nucleation mode to micrometer coarse mode particles. In contrast to other <i>a</i><sub>w</sub>-representations, our ν<sub><i>i</i></sub> factor corrects the solute molality both linearly and in exponent form <i>x · a<sup>x</sup></i>. We present four representations of our basic <i>a</i><sub>w</sub>-parameterization at different levels of complexity for different <i>a</i><sub>w</sub>-ranges, e.g. up to 0.95, 0.98 or 1. ν<sub><i>i</i></sub> is constant over the selected <i>a</i><sub>w</sub>-range, and in its most comprehensive form, the parameterization describes the entire <i>a</i><sub>w</sub> range (0–1). In this work we focus on single solute solutions. ν<sub><i>i</i></sub> can be pre-determined with a root-finding method from our water activity representation using an <i>a</i><sub>w</sub>−μ<sub>s</sub> data pair, e.g. at solute saturation using RHD and solubility measurements. Our <i>a</i><sub>w</sub> and supersaturation (Köhler-theory) results compare well with the thermodynamic reference model E-AIM for the key compounds NaCl and (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> relevant for CCN modeling and calibration studies. Envisaged applications include regional and global atmospheric chemistry and climate modeling

Viabilidade de pupas de Thelosia camina schaus coletadas em plantios de erva-mate.

Resumo

Platform Portable Anthropomorphic Grasping with the Bielefeld 20-DOF Shadow and 9-DOF TUM Hand

Röthling F, Haschke R, Steil JJ, Ritter H. Platform Portable Anthropomorphic Grasping with the Bielefeld 20-DOF Shadow and 9-DOF TUM Hand. In: Proc. Int. Conf. on Intelligent Robots and Systems (IROS). IEEE; 2007: 2951-2956

Adaptive scene dependent filters for segmentation and online learning of visual objects

Steil JJ, Götting M, Wersing H, Körner E, Ritter H. Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing. 2007;70(7-9):1235-1246

Detecting inhomogeneous chiral condensation from the bosonic two-point function in the (1+1)(1 + 1)-dimensional Gross-Neveu model in the mean-field approximation

The phase diagram of the $(1 + 1)$-dimensional Gross-Neveu model is reanalyzed for (non-)zero chemical potential and (non-)zero temperature within the mean-field approximation. By investigating the momentum dependence of the bosonic two-point function, the well-known second-order phase transition from the $\mathbb{Z}_2$ symmetric phase to the so-called inhomogeneous phase is detected. In the latter phase the chiral condensate is periodically varying in space and translational invariance is broken. This work is a proof of concept study that confirms that it is possible to correctly localize second-order phase transition lines between phases without condensation and phases of spatially inhomogeneous condensation via a stability analysis of the homogeneous phase. To complement other works relying on this technique, the stability analysis is explained in detail and its limitations and successes are discussed in context of the Gross-Neveu model. Additionally, we present explicit results for the bosonic wave-function renormalization in the mean-field approximation, which is extracted analytically from the bosonic two-point function. We find regions -- a so-called moat regime -- where the wave function renormalization is negative accompanying the inhomogeneous phase as expected.Comment: 27 pages (main text 20, appendix 7), 2 tables, 13 figures (plot data included in arXiv source file); Updated, published versio