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Tasks that require the cooperation of both hands and arms are common in human everyday life. Coordination helps to synchronize in space and temporally motion of the upper limbs. In fine bimanual tasks, coordination enables also to achieve higher degrees of precision that could be obtained from a single hand. We studied the acquisition of bimanual fine manipulation skills in watchmaking tasks, which require assembly of pieces at millimeter scale. It demands years of training. We contrasted motion kinematics performed by novice apprentices to those of professionals. Fifteen subjects, ten novices and five experts, participated in the study. We recorded force applied on the watch face and kinematics of fingers and arms. Results indicate that expert subjects wisely place their fingers on the tools to achieve higher dexterity. Compared to novices, experts also tend to align task-demanded force application with the optimal force transmission direction of the dominant arm. To understand the cognitive processes underpinning the different coordination patterns across experts and novice subjects, we followed the optimal control theoretical framework and hypothesize that the difference in task performances is caused by changes in the central nervous system’s optimal criteria. We formulated kinematic metrics to evaluate the coordination patterns and exploit inverse optimization approach to infer the optimal criteria. We interpret the human acquisition of novel coordination patterns as an alteration in the composition structure of the central nervous system’s optimal criteria accompanied by the learning process.

Objective: bioinformatic methods and molecular docking technology were used to predict the active components, targets, and related biological pathways of the Xiexin capsule in the intervention for dyslipidemia, exploring its mechanism. Methods: the active components and targets of the Xiexin capsule were screened by the TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform )database. Genecards (The Human Gene Database), OMIM (Online Mendelian Inheritance in Man), PharmGkb (Pharmacogenomics Knowledge Base database), TTD (Therapeutic Target Database), and Drugbank platforms were used to search the disease targets of dyslipidemia. The Cytoscape 3.8.0 software was used to construct the 'component-target' network diagram, and the STRING (functional protein association networks) platform was used to analyze protein-protein interaction (PPI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment analyses were performed by R language data packets to predict the mechanism of action. The AutoDockVina and PyMol software were used to dock the key active components in the Xiexin capsule and the core proteins in PPI. Results: a total of 66 effective components were screened, involving 114 targets; 87 key active compounds were screened from the 'drug-component-target' diagram. The PPI network mainly involved core proteins such as PTGS2 (prostaglandin-endoperoxide synthase 2), PTGS1 (prostaglandin-endoperoxide synthase 1), and HSP90AA1 (heat shock protein 90 alpha family class A member 1). GO and KEGG enrichment analysis results of common targets mainly involved hormone-mediated signaling pathway, steroid hormone response, lipid transport and metabolism, regulation of cholesterol storage, cyclooxygenase pathway, and other biological pathways, as well asMM PPAR (peroxisome proliferators-activated receptor) signaling pathway, IL-17 (interleukin 17) signaling pathway, PI3K-Akt (protein kinase b) signaling pathway, FcεRI signaling pathway, and other related pathways. Molecular docking verification showed that quercetin had the best binding with the core target protein HSP90AA1, and HSP90AA1 was the target protein with the best binding activity for the key chemical components in Xiexin capsules. Conclusion: the main chemical components in the Xiexin capsules may participate in the regulation of PPAR and other signaling pathways by regulating key genes such as ESR1 (estrogen receptor 1), MAPK14 (mitogen-activated protein kinase 14), and HSP90AA1, to exert the pharmacological effect of the intervention on dyslipidemia.Objetivo: se utilizaron métodos bioinformáticos y técnicas de acoplamiento molecular para predecir componentes efectivos, objetivos y vías biológicas relacionadas de la cápsula Xiexin en la intervención de dislipidemia y explorar su mecanismo. Métodos: los componentes activos y los objetivos de la cápsula Xiexin fueron seleccionados por la base de datos TCMSP. Se utilizaron plataformas Genecards, OMIM, PharmGkb, Therapeutic Target Database y Drugbank para buscar dianas de la enfermedad en la dislipidemia. El diagrama reticular "componente-diana" fue construido por el software Cytoscape 3.7.0, y la interacción proteína-proteína (PPI) fue analizada por la plataforma STRING. Los análisis de enriquecimiento de Gene Ontology (GO) y Kyoto Encyclopedia of Genes and Genomics se realizaron mediante paquetes de datos en lenguaje R para predecir mecanismo de acción. El software AutoDockVina y PyMol se utilizó para unir componentes activos clave de la cápsula Xiexin y las proteínas clave de la PPI. Resultados: se seleccionaron 65 componentes activos y 114 dianas. Veintitrés compuestos activos clave fueron seleccionados a partir de la tabla "componentes farmacéuticos-dianas". Las redes PPI incluyen principalmente proteínas básicas como PTGS2, PTGS1 y HSP90AA1. Los resultados del análisis de enriquecimiento de GO y KEGG en los objetivos comunes se refieren principalmente a la vía de señalización mediada por esteroides, la respuesta hormonal esteroidea, el transporte y metabolismo lipídicos, la regulación del almacenamiento de colesterol, la vía de la ciclooxigenasa y otras vías biológicas, así como la vía de señalización de PPAR, IL-17, PI3K-Akt, FcεRI y otras vías relacionadas. La prueba de acoplamiento molecular mostró que la quercetina se une mejor a la proteína diana central HSP90AA1, que es la proteína diana con la mejor actividad de unión de los componentes químicos clave de la cápsula Xiexin. Conclusión: los principales componentes químicos de la cápsula Xiexin pueden participar en la regulación de la PPAR y otras vías de señalización mediante la regulación de genes clave como ESR1, MAPK14 (mitogen-activated protein kinase 14), HSP90AA1, por lo que pueden desempeñar un papel farmacológico en la intervención de dislipidemia.

Core samples play a very important role in understanding the characteristics and conditions of rock formation. The coring tool is the primary equipment used to obtain a core sample, which has been widely used in engineering, and oil and gas drilling, for example. In this paper, the models of conventional coring joints and special coring joints are established to study the mechanical properties of the two joints being subjected to makeup torque load. The results show that the design of the special coring tool with double shoulders effectively shares the force on the thread and has a good mechanical performance compared with the conventional single-shoulder coring tool.

We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. We provide two setups that try to highlight the following challenges: (a) manipulating objects via a tool, (b) placing irregularly shaped objects in the correct groove, (c) handling semi-deformable objects, and (d) bimanual coordination. We provide CAD drawings of the task pieces that can be easily 3D printed to ensure ease of reproduction, and detailed description of tasks and protocol for successful reproduction, as well as meaningful metrics for comparison. We propose four categories of submission in an attempt to make the benchmark accessible to a wide range of related fields spanning from adaptive control, motion planning to learning the tasks through trial-and-error learning.

The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active touch objects learning method that enables the robot to learn efficiently about unknown objects via their physical properties (stiffness, surface texture, and center of mass); and (3) an active object recognition strategy, based on the knowledge the robot has acquired. Furthermore, we propose a tactile-based approach for estimating the center of mass of rigid objects. Following the active touch for workspace exploration, the robotic system with the sense of touch in fingertips reduces the uncertainty of the workspace up to 65 and 70% compared respectively to uniform and random strategies, for a fixed number of samples. By means of the active touch learning method, the robot achieved 20 and 15% higher learning accuracy for the same number of training samples compared to uniform strategy and random strategy, respectively. Taking advantage of the prior knowledge obtained during the active touch learning, the robot took up to 15% fewer decision steps compared to the random method to achieve the same discrimination accuracy in active object discrimination task.

In this letter, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: 1) an active pretouch strategy to efficiently explore unknown workspaces; 2) an active touch learning method to learn about unknown objects based on their physical properties (surface texture, stiffness, and thermal conductivity) with the least number of training samples; and 3) an active touch algorithm for object discrimination, which selects the most informative exploratory action to apply to the object, so that the robot can efficiently distinguish between objects with a few number of actions. Our proposed framework was experimentally evaluated using a robotic arm equipped with multimodal artificial skin. The robot with the active pretouch method reduced the uncertainty of the workspace up to 30% and 70% compared to uniform and random strategies, respectively. By means of the active touch learning algorithm, the robot used 50% fewer samples to achieve the same learning accuracy than the baseline methods. By taking advantage of the prior knowledge obtained during the learning process, the robot actively discriminated objects with an improvement of 10% recognition accuracy compare to the random action selection approach.

International audience; — In robotic tasks, object recognition and discrimination can be realized according to their physical properties, such as color, shape, stiffness, and surface textures. However, these external properties may fail if they are similar or even identical. In this case, internal properties of the objects can be considered, for example, the center of mass. Center of mass is an important inherent physical property of objects; however, due to the difficulties in its determination, it has never been applied in object discrimination tasks. In this work, we present a tactile-based approach to explore the center of mass of rigid objects and apply it in robotic object discrimination tasks. This work comprises three aspects: (a) continuous estimation of the target object's geometric information, (b) exploration of the center of mass, and (c) object discrimination based on the center of mass features. Experimental results show that by following our proposed approach, the center of mass of experimental objects can be accurately estimated, and objects of identical external properties but different mass distributions can be successfully discriminated. Our approach is also robust against the textural properties and stiffness of experimental objects.

International audience; Tactile sensing feedback provides feasible solutions to robotic dexterous manipulation tasks. In this paper, we present a novel tactile-based framework for detecting/correcting slips and regulating grasping forces while manipulating de-formable objects with the dynamic center of mass. This framework consists of a tangential force based slip detection method and a deformation prevention approach relying on weight estimation. Moreover, we propose a new strategy for manipulating deformable heavy objects. Objects with different stiffnesses, surface textures, and centers of mass are tested in experiments. Results show that proposed approaches are capable of handling objects with uncertainties in their characteristics, and also robust to external disturbances.