Measuring inefficiency in the rubber manufacturing industry

Malaysia is the fifth largest producer of natural rubber in the world after Thailand, Indonesia, Vietnam and China as well as producing rubber products exported to more than 190 countries worldwide. However, the slowdown in growth of major importers such as China, the European Union and the United States and the perception of stock surplus as output exceeds demand led to fluctuating rubber production performance over the period 2010 to 2016. Hence, this article aims at examining the level of technical efficiency (TE) and to analyze the determinants of the inefficiencies of the rubber manufacturing industry. The analysis was conducted using the latest 145 firms’ data obtained from the Department of Statistics Malaysia (DOS) and using the Stochastic Frontier Analysis (SFA) method. The results showed that the overall TE level was high while the determinants such as the capital-labor ratio, wage rate and firm size had a negative and significant impact that could reduce industrial technical efficiencies. The policy implication is that the rubber manufacturing industry needs to focus on high technological production investment, increase employee motivation through wage increment and create more strategic cooperation with international industry

視覚情報を用いた折り紙の袋折り手法の提案

本研究の目的は，折り紙ロボットによる袋折り作業を実現させることである． まず，本研究グループで作業記述に用いている折り紙公理によって，袋折り作業を表現する方法を検討した．2回の三角折りと2回の四角折りをそれぞれ初期状態とする2種類の袋折りを対象とし．三角折りからについては公理2，四角折りからについては公理3を適用することとした． 　利用する2つの公理のうち，公理2の実現については先行研究の「頂点合わせ」を利用できる．一方公理3については未着手であったため，2つの辺を重ね合わせる「辺合わせ」を提案した．辺合わせはカメラ画像から折り紙の辺の位置を検出し，それをロボットの手先動作にフィードバックさせることで行う．本研究では辺検出について，色相の差異を利用する手法と，折り紙の重なった部分にできる影を利用する手法の2つを提案した． 　次に，袋折り作業を実現するためのロボットの操作について検討を行った．人間が行う袋折り作業は，高精度なセンシングや手先位置の制御に支えられており，それをそのままロボットに実装するのは困難である．そこで，人間の作業手順をロボットが行いやすい形に分解，整理し，「共通スキル」として実装した．共通スキルは「ずらし操作」「クセ付け操作」「折り畳み操作」「折り線付け操作」の4つからなり，これと頂点合わせ，辺合わせを組み合わせることで袋折り作業を行う． 実験では，最初に提案した辺合わせの精度評価を行った．四角折り途中の状態から辺合わせを行い，おおよそ理論値通りの結果が得られた．次に袋折り作業の評価実験を行った．三角折りから，四角折りからのそれぞれについて提案手法を用いて袋折り作業を行った．その結果，辺検出の精度や折り線付け操作の方法について課題は見つかったものの，袋折り作業を実現し，提案手法の有用性が確認できた.電気通信大学201

Folding Paper with Anthropomorphic Robot Hands using Real-Time Physics-Based Modeling

Elbrechter C, Haschke R, Ritter H. Folding Paper with Anthropomorphic Robot Hands using Real-Time Physics-Based Modeling. In: 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). Piscataway, NJ: IEEE; 2012.The ability to manipulate deformable objects, such as textiles or paper, is a major prerequisite to bringing the capabilities of articulated robot hands closer to the level of manual intelligence exhibited by humans. We concentrate on the manipulation of paper, which affords us a rich interaction domain that has not yet been solved for anthropomorphic robot hands. Robust tracking and physically plausible modeling of the paper as well as feedback based robot control are crucial components for this task. This paper makes two novel contributions to this area. The first concerns real-time modeling and visual tracking. Our technique not only models the bending of a sheet of paper, but also paper crease lines which allows us to monitor deformations. The second contribution concerns enabling an anthropomorphic robot to fold paper, and is accomplished by introducing a set of tactile- and vision-based closed loo

Active compliance control strategies for multifingered robot hand

Safety issues have to be enhanced when the robot hand is grasping objects of different shapes, sizes and stiffness. The inability to control the grasping force and finger stiffness can lead to unsafe grasping environment. Although many researches have been conducted to resolve the grasping issues, particularly for the object with different shape, size and stiffness, the grasping control still requires further improvement. Hence, the primary aim of this work is to assess and improve the safety of the robot hand. One of the methods that allows a safe grasping is by employing an active compliance control via the force and impedance control. The implementation of force control considers the proportional–integral–derivative (PID) controller. Meanwhile, the implementation of impedance control employs the integral slidingmode controller (ISMC) and adaptive controller. A series of experiments and simulations is used to demonstrate the fundamental principles of robot grasping. Objects with different shape, size and stiffness are tested using a 3-Finger Adaptive Robot Gripper. The work introduces the Modbus remote terminal unit [RTU] protocol, a low-cost force sensor and the Arduino IO Package for a real-time hardware setup. It is found that, the results of the force control via PID controller are feasible to maintain the grasped object at certain positions, depending on the desired grasping force (i.e., 1N and 8N). Meanwhile, the implementation of impedance control via ISMC and adaptive controller yields multiple stiffness levels for the robot fingers and able to reduce collision between the fingers and the object. However, it was found that the adaptive controller produces better impedance control results as compared to the ISMC, with a 33% efficiency improvement. This work lays important foundations for long-term related research, particularly in the field of active compliance control that can be beneficial to human–robot interaction (HRI)

Enhancing reinforcement learning with a context-based approach

Reinforcement Learning (RL) has shown outstanding capabilities in solving complex computational problems. However, most RL algorithms lack an explicit method for learning from contextual information. In reality, humans rely on context to identify patterns and relations among elements in the environment and determine how to avoid making incorrect actions. Conversely, what may seem like obvious poor decisions from a human perspective could take hundreds of steps for an agent to learn how to avoid them. This thesis aims to investigate methods for incorporating contextual information into RL in order to enhance learning performance. The research follows an incremental approach in which, first, contextual information is incorporated into RL in simulated environments, more concisely in games. The experiments show that all the algorithms which use contextual information significantly outperform the baseline algorithms by 77 % on average. Then, the concept is validated with a hybrid approach that comprises a robot in a Human-Robot Interaction (HRI) scenario dealing with rigid objects. The robot learns in simulation while executing actions in the real world. For this setup, based on contextual information, the proposed algorithm trains in a reduced amount of time (2.7 seconds). It reaches an 84% success rate in a grasp and release-related task while interacting with a human user, while the baseline algorithm with the highest success rate reached 68% after learning during a significantly longer period of time (91.8 seconds). Consequently, CQL suits the robot’s learning requirements in observing the current scenario configuration and learning to solve it while dealing with dynamic changes provoked by the user. Additionally, the thesis explores using an RL framework that uses contextual information to learn how to manipulate bags in the real world. A bag is a deformable object that presents challenges from grasping to planning, and RL has the potential to address this issue. The learning process is accomplished through a new RL algorithm introduced in this work called Π-learning, designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilises a set of primitive actions and represents the task in five states. In the experiments, the framework reaches a 60% and 80% success rate after around three hours of training in the real world when starting the bagging task from folded and unfolded positions, respectively. Finally, the trained model is tested on two more bags of different sizes to evaluate its generalisation capacities. Overall, this research seeks to contribute to the broader advancement of RL and robotics, aiming to enhance the development of intelligent, autonomous systems that can effectively operate in diverse and dynamic real-world settings. Besides that, this research seeks to explore new possibilities for automation, HRI, and the utilisation of contextual information in RL