Generation of Tactile Data from 3D Vision and Target Robotic Grasps

Tactile perception is a rich source of information for robotic grasping: it allows a robot to identify a grasped object and assess the stability of a grasp, among other things. However, the tactile sensor must come into contact with the target object in order to produce readings. As a result, tactile data can only be attained if a real contact is made. We propose to overcome this restriction by employing a method that models the behaviour of a tactile sensor using 3D vision and grasp information as a stimulus. Our system regresses the quantified tactile response that would be experienced if this grasp were performed on the object. We experiment with 16 items and 4 tactile data modalities to show that our proposal learns this task with low error.This work was supported in part by the Spanish Government and the FEDER Funds (BES-2016-078290, PRX19/00289, RTI2018-094279-B-100) and in part by the European Commission (COMMANDIA SOE2/P1/F0638), action supported by Interreg-V Sudoe

Meso-microscale coupling for wind resource assessment using averaged atmospheric stability conditions

This research was supported by a grant from The Norwegian Research Council, project number 271080. We acknowledge Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project. We would like to thank the High Performance Computing Center North (HPC2N) for providing the computer resources needed to perform the numerical experiments presented in this paper. We would also like to thank the two anonymous reviewers for their useful comments.Peer reviewedPublisher PD

Phoebe 2.0 – Triple and multiple systems

Some close binary formation theories require the presence of a third body so that the binary orbit can shrink over time. Tidal friction and Kozai cycles transfer energy from the binary to its companion, resulting in a close inner binary and a wide third body orbit. Spectroscopy and imaging studies have found 40% of binaries with periods less than 10 days, and 96% with periods less than 3 days, have a wide tertiary companion. With recent advancements in large photometric surveys, we are now beginning to detect many of these triple systems by observing tertiary eclipses or through the effect they have on the eclipse timing variations (ETVs) of the inner-binary. In the sample of 2600 Kepler EBs, we have detected the possible presence of a third body in ∼20%, including several circumbinary planets. Some multiple systems are quite dynamical and feature disappearing and reappearing eclipses, apsidal motion, and large disruptions to the inner-binary. phoebe is a freely available binary modeling code which can dynamically model all of these systems, allowing us to better test formation theories and probe the physics of eclipsing binaries

Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection

Robotic manipulators have to constantly deal with the complex task of detecting whether a grasp is stable or, in contrast, whether the grasped object is slipping. Recognising the type of slippage—translational, rotational—and its direction is more challenging than detecting only stability, but is simultaneously of greater use as regards correcting the aforementioned grasping issues. In this work, we propose a learning methodology for detecting the direction of a slip (seven categories) using spatio-temporal tactile features learnt from one tactile sensor. Tactile readings are, therefore, pre-processed and fed to a ConvLSTM that learns to detect these directions with just 50 ms of data. We have extensively evaluated the performance of the system and have achieved relatively high results at the detection of the direction of slip on unseen objects with familiar properties (82.56% accuracy).Work funded by the Spanish Ministry of Economy, Industry and Competitiveness, through the project DPI2015-68087-R (predoctoral grant BES-2016-078290) as well as the European Commission and FEDER funds through the COMMANDIA (SOE2/P1/F0638) action supported by Interreg-V Sudoe

Predicción de la Estabilidad en Tareas de Agarre Robótico con Información Táctil

En tareas de manipulación robótica es de especial interés detectar si un agarre es estable o por el contrario, el objeto agarrado se desliza entre los dedos debido a un contacto inadecuado. Con frecuencia, la inestabilidad en el agarre puede ser como consecuencia de una mala pose de la mano o pinza robótica durante su ejecución y o una presión de contacto insuficiente a la hora de ejercer la tarea. El empleo de información táctil y la representación de ésta es vital para llevar a cabo la predicción de estabilidad en el agarre. En este trabajo, se presentan y comparan distintas metodologías para representar la información táctil, así como los métodos de aprendizaje más adecuados en función de la representación táctil escogida.Este trabajo ha sido financiado con Fondos Europeos de Desarrollo Regional (FEDER), Ministerio de Economía, Industria y Competitividad a través del proyecto RTI2018-094279-B-100 y la ayuda predoctoral BES-2016-078290, y también gracias al apoyo de la Comisión Europea y del programa Interreg V. Sudoe a través del proyecto SOE2/P1/F0638

Tactile-Driven Grasp Stability and Slip Prediction

One of the challenges in robotic grasping tasks is the problem of detecting whether a grip is stable or not. The lack of stability during a manipulation operation usually causes the slippage of the grasped object due to poor contact forces. Frequently, an unstable grip can be caused by an inadequate pose of the robotic hand or by insufficient contact pressure, or both. The use of tactile data is essential to check such conditions and, therefore, predict the stability of a grasp. In this work, we present and compare different methodologies based on deep learning in order to represent and process tactile data for both stability and slip prediction.Work funded by the Spanish Ministries of Economy, Industry and Competitiveness and Science, Innovation and Universities through the grant BES-2016-078290 and the project RTI2018-094279-B-100, respectively, as well as the European Commission and FEDER funds through the COMMANDIA project (SOE2/P1/F0638), action supported by Interreg-V Sudoe

Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?

Tactile sensors supply useful information during the interaction with an object that can be used for assessing the stability of a grasp. Most of the previous works on this topic processed tactile readings as signals by calculating hand-picked features. Some of them have processed these readings as images calculating characteristics on matrix-like sensors. In this work, we explore how non-matrix sensors (sensors with taxels not arranged exactly in a matrix) can be processed as tactile images as well. In addition, we prove that they can be used for predicting grasp stability by training a Convolutional Neural Network (CNN) with them. We captured over 2500 real three-fingered grasps on 41 everyday objects to train a CNN that exploited the local connectivity inherent on the non-matrix tactile sensors, achieving 94.2% F1-score on predicting stability

Localized thinning for strain concentration in suspended germanium membranes and optical method for precise thickness measurement

We deposited Ge layers on (001) Si substrates by molecular beam epitaxy and used them to fabricate suspended membranes with high uniaxial tensile strain. We demonstrate a CMOS-compatible fabrication strategy to increase strain concentration and to eliminate the Ge buffer layer near the Ge/Si hetero-interface deposited at low temperature. This is achieved by a two-steps patterning and selective etching process. First, a bridge and neck shape is patterned in the Ge membrane, then the neck is thinned from both top and bottom sides. Uniaxial tensile strain values higher than 3% were measured by Raman scattering in a Ge membrane of 76 nm thickness. For the challenging thickness measurement on micrometer-size membranes suspended far away from the substrate a characterization method based on pump-and-probe reflectivity measurements was applied, using an asynchronous optical sampling technique.EC/FP7/628197/EU/Heat Propagation and Thermal Conductivity in Nanomaterials for Nanoscale Energy Management/HEATPRONAN

PHOEBE 2.0 – Where no model has gone before

phoebe 2.0 is an open source framework bridging the gap between stellar observations and models. It allows to create and fit models simultaneously and consistently to a wide range of observational data such as photometry, spectroscopy, spectrapolarimetry, interferometry and astrometry. To reach the level of precision required by the newest generation of instruments such as Kepler, GAIA and the arrays of large telescopes, the code is set up to handle a wide range of phenomena such as multiplicity, rotation, pulsations and magnetic fields, and to model the involved physics to a new level

Measurement of the 140Ce(n,γ) Cross Section at n_TOF and Its Astrophysical Implications for the Chemical Evolution of the Universe

140Ce(n, γ) is a key reaction for slow neutron-capture (s-process) nucleosynthesis due to being a bottleneck in the reaction flow. For this reason, it was measured with high accuracy (uncertainty ≈5%) at the n_TOF facility, with an unprecedented combination of a high purity sample and low neutron-sensitivity detectors. The measured Maxwellian averaged cross section is up to 40% higher than previously accepted values. Stellar model calculations indicate a reduction around 20% of the s-process contribution to the Galactic cerium abundance and smaller sizeable differences for most of the heavier elements. No variations are found in the nucleosynthesis from massive stars.U.S. National Science Foundation (Grants No. AST 1613536, No. AST 1815403/1815767, No. AST 2205847, and No. PHY 1430152—Joint Institute for Nuclear Astrophysics—Chemical Evolution of the Elements)European Union—NextGenerationEU RFF M4C2 1.1 PRIN 2022 project “2022RJLWHN URKA”INAF Theory Grant “Understanding R-process & Kilonovae Aspects (URKA)”MSMTof the Czech Republic, the Charles University UNCE/SCI/013 projec