Bootstrapping Robotic Skill Learning With Intuitive Teleoperation: Initial Feasibility Study

Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to generate high-quality human demonstrations efficiently for robotic skill learning in a data-driven manner. By using a reliable teleoperation interface, the da Vinci Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration (dS4D) is proposed in this paper. Various manipulation tasks show the system's effectiveness and advantages in efficiency compared to other interfaces. Using the collected data for policy learning has been investigated, which verifies the initial feasibility. We believe the proposed paradigm can facilitate robot learning driven by high-quality demonstrations and efficiency while generating them.Comment: 10 pages, 4 figures, accepted by ISER202

Correspondencia entre Lin Shu y Cai Yuanpei relativa al movimiento de la nueva cultura (marzo-abril, 1919)

Lin Shu 林紓 (8 de noviembre de 1852-9 de octubre de 1924),² nombre de cortesía Qinnan 琴南, literato y traductor nacido en Nantai 南台, en la antigua prefectura Min 閩 (actual Fuzhou 福州, Fujian), en el seno de una familia de comerciantes con escasos recursos económicos. Ya desde los cuatro años comenzó su formación en los clásicos confucianos, que fue fundamen­talmente autodidacta y dependió en gran medida de los pocos textos que conservaba su tío, así como de libros usados que el joven Lin Shu compraba con el dinero que su madre le daba para comer. Comenzó con veinte años su carrera docente en una escuela local, preparándose mientras tanto para los exámenes imperiales. No fue sino hasta los 40 años de edad que Lin Shu entró en contacto con la literatura de las dinastías Tang y Song, influencia decisiva en su posterior tarea de tra­ductor. Con el fallecimiento de su primera esposa, en 1897, Lin Shu, aconsejado por varios amigos, inició la traducción de La Dama de las Camelias de Alejandro Dumas. La novela apareció publicada en la primavera de 1899 y constituyó una revolución literaria en todo el país. A partir de entonces, Lin Shu se convirtió en el traductor por excelencia, y llegó a pu­blicar, se estima, 213 traducciones de obras occidentales.³ Esta cuantiosa producción literaria era fruto del esfuerzo mutuo de un grupo de amigos versados en lenguas extranjeras que traducían oralmente a la lengua vernácula o baihua 白話 estas novelas, para ser inmediatamente reescritas en lengua culta o wenyan 文言 por Lin Shu

Zhongguo li shi jiao ke shu.

Cover title: Ding zheng chu deng xiao xue Zhongguo li shi jiao ke shu.At head of title: Zui xinMode of access: Internet

Zhe xue da gang /

Fu: Yi yu zhi yao.Chü Te-kuo che hsüeh chia Li-hsi-tʻo (Richter) ti "Che hsüeh tao yen" ("Einführung in die Philosophie") i chi Pʻao-erh-sheng (Paulsen) ho Feng-tʻe (Wunde) ti "Che hsüeh ju men" ("Einleitung in die Philosophie") pien hsieh erh chʻeng ti shih fan hsüeh hsiao chiao tsʻai.Colophon title.Mode of access: Internet

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky–Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm−1 and 3017~2823 cm−1/1805~1734 cm−1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible