A developmental approach to robotic pointing via human–robot interaction

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/)The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot's initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human-robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. Developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants

A developmental approach to robotic pointing via human-robot interaction

The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot's initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human-robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. Developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants

A Reduced Classifier Ensemble Approach to Human Gesture Classification for Robotic Chinese Handwriting

The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character’s strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble’s size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke’s trajectory to the robot’s motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters

Upregulation of MIAT Regulates LOXL2 Expression by Competitively Binding MiR-29c in Clear Cell Renal Cell Carcinoma

Background/Aims: MIAT is a long noncoding RNA (lncRNA) involved in cell proliferation and the development of tumor. However, the exact effects and molecular mechanisms of MIAT in clear cell renal cell carcinoma (ccRCC) progression are still unknown. Methods: We screened the lncRNAs’ profile of ccRCC in The Cancer Genome Atlas database, and then examined the expression levels of lncRNA MIAT in 45 paired ccRCC tissue specimens and in cell lines by q-RT-PCR. MTS, colony formation, EdU, and Transwell assays were performed to examine the effect of MIAT on proliferation and metastasis of ccRCC. Western blot and luciferase assays were performed to determine whether MIAT can regulate Loxl2 expression by competitively binding miR-29c in ccRCC. Results: MIAT was up-regulated in ccRCC tissues and cell lines. High MIAT expression correlated with worse clinicopathological features and shorter survival rate. Functional assays showed that knockdown of MIAT inhibited renal cancer cell proliferation and metastasis in vitro and in vivo. Luciferase and western blot assays further confirmed that miR-29c binds with MIAT. Additionally, the correlation of miR-29c with MIAT and Loxl2 was further verified in patients' samples. Conclusion: Our data indicated that MIAT might be an oncogenic lncRNA that promoted proliferation and metastasis of ccRCC, and could be a potential therapeutic target in human ccRCC

Integration of Brain-like neural network and infancy behaviors for robotic pointing

Conference Name:2014 International Conference on Information Science, Electronics and Electrical Engineering, ISEEE 2014. Conference Address: Sapporo City, Hokkaido, Japan. Time:April 26, 2014 - April 28, 2014.Future University Hakodate; IEEE Sapporo Section; Xiamen UniversityThis paper introduces a new approach to learning pointing behavior in a developmental robot by using a type of constructive neural network and Q-learning algorithm, taking inspirations from human infant development. The pointing behavior is considered as the first movement that human infants use to communicate with other person during human development, it is also the foundation of the human social interaction abilities. We rebuilt this developmental course in our robot simulation system. The learning algorithm of the pointing is implemented by Q-Learning, and a radial based function neural network with resource allocating algorithm is applied to hold the learning result and to control robot movements. The experimental results show that the approach is able to lead our development robot to generate pointing behavior

The Relationship between Natural Pyrite and Impurity Element Semiconductor Properties: A Case Study of Vein Pyrite from the Zaozigou Gold Deposit in China

Pyrite is a common sulfide mineral in gold deposits, and its unique thermoelectricity has received extensive attention in the field of gold exploration. However, there is still a lack of detailed research and direct evidence about how impurity elements affect mineral semiconductor properties. In this paper, combined with first-principles calculations, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) mapping technology and in situ Seebeck coefficient scanning probe technology were used to study the law of changing semiconductor properties in pyrite containing impurity elements such as As, Co, Ni, and Cu. The results showed that pyrite containing arsenic is a P-type semiconductor, and pyrites containing Ni, Co, Cu, and other elements are N-type semiconductors. When P-type pyrites containing As were supplemented with Ni, Cu, and other elements, the semiconductor type changed to N-type. However, Co addition did not change the semiconductor type of arsenic-rich pyrite. Pyrite formed under different temperature conditions tended to be enriched with different combinations of impurity elements, leading to the relative accumulation of P-type or N-type pyrites

Retrieving Three-Dimensional Large Surface Displacements in Coal Mining Areas by Combining SAR Pixel Offset Measurements with an Improved Mining Subsidence Model

Interferometric synthetic aperture radar (InSAR) technology can obtain one-dimensional surface displacements in the radar line of sight (LOS). In the field of mining subsidence, large 3D movements often occur at the same time, and hence InSAR derived one-dimensional LOS displacements can hardly reflect the actual surface motion in mining areas. To realize the monitoring of three-dimensional large surface displacements in mining areas, a method for monitoring three-dimensional large surface displacements in mining areas that combines SAR pixel offset tracking (OT) and an improved mining subsidence model is proposed in this article. First, a new functional relationship between surface subsidence and horizontal movement combined with the characteristics of the overburden rock stress and the deformation characteristics of the fractured rock mass in coal mining areas is established. Then, a three-dimensional surface deformation model is established based on the proposed relationship between surface subsidence and horizontal movement and the radar projection equation, and finally, the optimal parameters of the deformation model are inverted iteratively using LOS deformation results obtained by OT method to retrieve the three-dimensional large displacements of the surface. The significant advantage of the method proposed in this article is that it can accurately acquire the 3D large surface displacements using only two SAR amplitude images with the same imaging geometry. To verify the accuracy and reliability of the proposed algorithm, two scenes of high-resolution spotlight TerraSAR-X images are used in this paper to conduct a three-dimensional surface displacement monitoring experiment on a working panel in the Daliuta mining area in Shaanxi Province, China, based on the proposed method. Experimental monitoring results show that the maximum surface subsidence is approximately 4.5 m, and the maximum horizontal movements in the strike and dip directions are approximately 1.4 m and 1.2 m, respectively. Using GPS measurements to verify the monitoring results, the root mean square error (RMSE) of vertical subsidence is 6.8 cm, and the RMSE of horizontal movement is 7.1 cm. Compared with those in the original mining subsidence model, the accuracies of vertical subsidence and horizontal movement in the proposed model are increased by 28.2% and 37.5%, respectively, which proves the reliability and accuracy of the proposed method

Electrospun γ-Fe2O3 nanofibers as bioelectrochemical sensors for simultaneous determination of small biomolecules

Nanofibers of alpha-Fe2O3 and gamma-Fe2O3 have been obtained after the controlled calcination of precursor nanofibers synthesized by electrospinning. alpha-Fe2O3 nanofibers showed an irregular toruloid structure due to the decomposition of poly (4-vinyl) pyridine in air while gamma-Fe2O3 nanoparticles decorated nanofibers were observed after the calcination under N-2 atmosphere. Electrochemical measurements showed that different electrochemical behaviors were observed on the glassy carbon electrodes modified by alpha-Fe2O3 and gamma-Fe2O3 nanofibers. The electrode modified by gamma-Fe2O3 nanofibers exhibited high electrocatalytic activities toward oxidation of dopamine, uric acid and ascorbic acid while alpha-Fe2O3 nanofibers cannot. Furthermore, the gamma-Fe2O3 modified electrode can realize the selective detection of biomolecules in ternary electrolyte solutions. The synthesis of nanofibers of alpha-Fe2O3 and gamma-Fe2O3 and their electrochemical sensing properties relationship have been discussed and analyzed based on the experimental results. (C) 2018 Elsevier B.V. All rights reserved

A human-like learning approach to developmental robotic reaching

Conference Name:2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013. Conference Address: Shenzhen, China. Time:December 12, 2013 - December 14, 2013.This paper presents a human-like approach for robot to obtain reaching ability autonomously in three-dimensional environment. The essential elements of the approach are inspired by current findings in neural science research and developmental psychology. By imitating the mechanism of the infant realizing the body induction, the robot system realizes the automatic separation of the mechanical arm and the external environment. We propose a simulated retina visual structure to compress images and improve the robot efficiency. After separating the arm from the external environment, the robot establishes the model of the mechanical arm, and uses the 'Minimal Resource Allocation Neural Network' to implement the robot's learning system. A developmental constraint implemented mechanism is applied to the robot system, so that, the robot adapts to the environment and completes the tasks in dynamic environment step by step. The experiments and simulations demonstrate that the robotic system, by imitating the process of the human development, gradually obtains the reaching ability. ? 2013 IEEE