Robot-object contact perception using symbolic temporal pattern learning

This paper investigates application of machine learning to the problem of contact perception between a robots gripper and an object. The input data comprises a multidimensional time-series produced by a force/torque sensor at the robots wrist, the robots proprioceptive information, namely, the position of the end-effector, as well as the robots control command. These data are used to train a hidden Markov model (HMM) classifier. The output of the classifier is a prediction of the contact state, which includes no contact, a contact aligned with the central axis of the valve, and an edge contact. To distinguish between contact states, the robot performs exploratory behaviors that produce distinct patterns in the time-series data. The patterns are discovered by first analyzing the data using a probabilistic clustering algorithm that transforms the multidimensional data into a one-dimensional sequence of symbols. The symbols produced by the clustering algorithm are used to train the HMM classifier. We examined two exploratory behaviors: a rotation around the x-axis, and a rotation around the y-axis of the gripper. We show that using these two exploratory behaviors we can successfully predict a contact state with an accuracy of 88 ± 5 % and 81 ± 10 %, respectively

Controlled Tactile Exploration and Haptic Object Recognition

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects.We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method

Intelligent Traffic Management and Load Balance Based on Spike ISDN-IoT

Ministry of Higher Education and Scientific Research, University of Baghdad, Ira

A log analysis study of 10 years of ebook consumption in academic library collections

Even though libraries have been offering eBooks for more than a decade, very little is known about eBook access and consumption in academic library collections. This paper addresses this gap with a log analysis study of eBook access at the library of the University of Waikato. This in-depth analysis covers a period spanning 10 years of eBook use at this university. We draw conclusions about the use of eBooks at this institution and compare the results with other published studies of eBook usage at tertiary institutes

Underwater robot-object contact perception using machine learning on force/torque sensor feedback

Autonomous manipulation of objects requires re- liable information on robot-object contact state. Underwater environments can adversely affect sensing modalities such as vision, making them unreliable. In this paper we investi- gate underwater robot-object contact perception between an autonomous underwater vehicle and a T-bar valve using a force/torque sensor and the robot’s proprioceptive information. We present an approach in which machine learning is used to learn a classifier for different contact states, namely, a contact aligned with the central axis of the valve, an edge contact and no contact. To distinguish between different contact states, the robot performs an exploratory behavior that produces distinct patterns in the force/torque sensor. The sensor output forms a multidimensional time-series. A probabilistic clustering algo- rithm is used to analyze the time-series. The algorithm dissects the multidimensional time-series into clusters, producing a one- dimensional sequence of symbols. The symbols are used to train a hidden Markov model, which is subsequently used to predict novel contact conditions. We show that the learned classifier can successfully distinguish the three contact states with an accuracy of 72% ± 12 %

Adenosine receptor mediates nicotine-induced antinociception in formalin test

In this study, the effect of adenosine receptor agents on nicotine induced antinociception, in formalin test, has been investigated. Intraperitoneal (i.p.) administration of different doses of nicotine (0.1, 1, 10 and 100 μg kg -1) induced a dose-dependent antinociception in mice, in the both first and second phases of the test. Adenosine receptor antagonist, theophylline (5, 10, 20 and 80 mg kg-1, i.p.) also induced antinociception in the both phases, while a dose of the drug (40 mg kg-1, i.p.) did not induce any response. Theophylline reduced antinociception induced by nicotine in both phases of formalin test. The A2 receptor agonist, 5�-N-ethylcarboxamide adenosine (NECA; 1 and 5 μg kg-1, i.p.) also produced antinociception, which was reversed with different doses of theophylline (5, 10, 20 and 40 mg kg-1, i.p.). But administration of the adenosine receptor agonist, NECA did not potentiate the response of nicotine. It is concluded that adenosine system may be involved in modulation of antinociception induced by nicotine. © 2004 Elsevier Ltd. All rights reserved