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

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

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

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

Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot

Reinhart F, Steil JJ. Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. In: Procedia Technology. Vol 26. 2016: 12-19

Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks

Aswolinskiy W, Steil JJ. Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks. In: Proceedings of the Workshop on New Challenges in Neural Computation. Machine Learning Reports. 2016