Dexterity and versatility in the pinching motion of robot fingers with multi-degrees of freedom

This paper focuses on dexterity and versatility in pinching a rectangular object by a pair of robot fingers based on sensory feedback. In the pinching motion of humans, it is possible to execute concurrent pinching and orientation control quickly and precisely by using only the thumb and index finger. However, it is not easy for robot fingers to perform such imposed tasks agilely and simultaneously. In the case of robotic grasping, to perform concurrently such plural tasks retards the convergence speed in the execution of the overall task. This means that in order to increase versatility by imposing additional tasks, dexterity in the execution of each task may deteriorate. In this paper it is shown that both dexterity and versatility in the execution of such imposed tasks can be enhanced remarkably, without any deterioration in dexterity in the execution of each task, by using a sensory feedback method based on the idea of role-sharing joint control which comes from observation of the functional role of each human finger joint.     

Skill learning framework for human–robot interaction and manipulation tasks

In this article, a learning framework that enables robotic arms to replicate new skills from human demonstration is proposed. The learning framework makes use of online human motion data acquired using wearable devices as an interactive interface for providing the anticipated motion to the robot in an efficient and user-friendly way. This approach offers human tutors the ability to control all joints of the robotic manipulator in real-time and able to achieve complex manipulation. The robotic manipulator is controlled remotely with our low-cost wearable devices for easy calibration and continuous motion mapping. We believe that our approach might lead to improving the human-robot skill learning, adaptability, and sensitivity of the proposed human-robot interaction for flexible task execution and thereby giving room for skill transfer and repeatability without complex coding skills.     

Comparative evaluation of performance measures for human driving skills

In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.     

Skill acquisition system for the intelligent assisting system_IAS

^_In this paper we propose a new forthcoming research topic, the Intelligent Assisting System^_ IAS. Using this system, we are approaching the identification and analysis of human manipulation skills to be used for intelligent human operator assistance. A manipulation skill database enables the IAS to perform complex manipulations at the motion control level. Through repeated interaction with the operator for unknown environment states, the manipulation skills in the database can be increased on-line. A model for manipulation skill based on the grip transformation matrix is proposed, which describes the transformation between the object trajectory and the contact conditions. The dynamic behaviour of the grip transform is regarded as the essence of the performed manipulation skill. We describe the experimental system set-up of a skill acquisition and transfer system as a first approach to the IAS. A simple example of manipulation shows the feasibility of the proposed manipulation skill model. Furthermore, this paper derives a control algorithm that realizes object task trajectories, and its feasibility is shown by simulation.     

Acquisition of a page turning skill for a multifingered hand using reinforcement learning

This paper proposes a method of acquisition of a page turning skill for a multifingered robotic hand using reinforcement learning. The goal of this paper is generation of the manipulation skill of a flexible object without its explicit model and tactile sensation during its control. In this paper, a page turning task is considered as an example of such tasks, where a sheet of paper, generally used in books, is a flexible object. The fingertip trajectories are obtained by reinforcement learning based on simulation. The reward considering a friction condition is given, so that a page turning skill without slip between the finger and the paper is obtained. The validity is confirmed by an experimental system which consists of a 2-d.o.f. manipulator and two 2-d.o.f. fingers.     

时间Petri网的可调度分析及在FMS中的应用

在实时系统中,检查任务执行的计划是否满足要求的时间约束称为可调度分析.通过把时间特性与其他行为特性分离,提出了一种以时间Petri网建模的实时系统调度分析方法.如果特定任务的执行是可调度的,则可以计算任务执行的时间跨度,否则确定出不可调度的变迁以便于调整时间约束和纠正设计错误.提出了一种通过把复杂的任务序列分解成一些子序列来进行可调度性分析的综合时序分析技术,它不仅提高了效率,也有助于关于调度的可达性问题的讨论.讨论了柔性制造系统FMS中的车间装配子系统的可调度性.     

A comparative study of information input devices for aging computer users

The fast aging of many western and eastern societies and their increasing reliance on information technology create a compelling need to reconsider older users' interactions with computers. Changes in perceptual and motor skill abilities that often accompany the aging process have important implications for the design of information input devices. This paper summarises the results of two comparative studies on information input with 90 subjects aged between 20 and 75 years. In the first study, three input devices – mouse, touch screen and eye-gaze control – were analysed concerning efficiency, effectiveness and subjective task difficulty with respect to the age group of the computer user. In the second study, an age-differentiated analysis of hybrid user interfaces for input confirmation was conducted combining eye-gaze control with additional input devices. Input confirmation was done with the space bar of a PC keyboard, speech input or a foot pedal. The results of the first study show that regardless of participants' age group, the best performance in terms of short execution time results from touch screen information input. This effect is even more pronounced for the elderly. Regarding the hybrid interfaces, the lowest mean execution time, error rate and task difficulty were found for the combination of eye-gaze control with the space bar. In conclusion, we recommend using direct input devices, particularly a touch screen, for the elderly. For user groups with severe motor impairments, we suggest eye-gaze information input.     

Fine motion strategy for skill-based manipulation

Generally, a manipulator task can be divided into several motion primitives called “skills.” Skill-based motion planning is an effective way to execute a complicated task. When planning an assembly process, a technique of fine motion planning such as the backprojection method in configuration space is often used. This paper describes fine motion planning using a skill library, which consists of a pattern of trajectories of skill motions in configuration space. This method gives the initial position and orientation from which the object can reach the goal in skill-based manipulation.     

Low-cost static performance prediction of parallel stochastic task compositions

Current analytic solutions to the execution time distribution of a parallel composition of tasks having stochastic execution times are computationally complex, except for a limited number of distributions. In this paper, we present an analytical solution based on approximating execution time distributions in terms of the first four statistical moments. This low-cost approach allows the parallel execution time distribution to be approximated at ultra-low solution complexity for a wide range of execution time distributions. The accuracy of our method is experimentally evaluated for synthetic distributions as well as for task execution time distributions found in real parallel programs and kernels (NAS-EP, SSSP, APSP, Splash2-Barnes, PSRS, and WATOR). Our experiments show that the prediction error of the mean value of the parallel execution time for N-ary parallel composition is in the order of percents, provided the task execution time distributions are sufficiently independent and unimodal.     

Task identification and characterisation in mobile robotics through non-linear modelling

The lack of a theory-based design methodology for mobile robot control programs means that control programs have to be developed through an empirical trial-and-error process. This can be costly, time consuming and error prone.In this paper we show how to develop a theory of robot–environment interaction, which would overcome the above problem. We show how we can model a mobile robot’s task (so-called “task identification”) using non-linear polynomial models (NARMAX), which can subsequently be formally analysed using established mathematical methods. This provides an understanding of the underlying phenomena governing the robot’s behaviour.Apart from the paper’s main objective of formally analysing robot–environment interaction, the task identification process has further benefits, such as the fast and convenient cross-platform transfer of robot control programs (“Robot Java”), parsimonious task representations (memory issues) and very fast control code execution times.     

同构Hadoop环境作业执行时间计算方法

执行时间是作业调度的重要参考因素之一。通过分析Hadoop MapReduce环境作业的执行特征,提出了以map任务和reduce任务执行时间为输入,估算作业执行时间的方法。该方法在一定假设条件下,借助作业预执行来获取map任务和reduce任务的执行时间。实验结果表明,该方法估算作业执行时间的误差率小于7%。     

An analysis on secure coding using symbolic execution engine

Business’ dependency on a software or computer program is getting higher. In such an environment, eliminating security vulnerabilities have become increasingly important and difficult as programs are more complicated and have greater impacts on businesses. We analyzed the security vulnerabilities of code using a symbolic execution engine that tracks data which would kill or might make the program vulnerable. We also present smart fuzzing using the data from the symbolic execution engine, an effective software vulnerability-finding testing that automatically generates inputs that crash or penetrate the program. By using symbolic execution engine, we can produce the automatically-generated data that are strong against vulnerability issues. In the case when program verification tools fail to verify a program, either the program is buggy or the report is a false alarm. In this case, the burden is put on users in manually classifying the report, which is a time-consuming, error-prone task and it does not utilize facts already proven by the analysis. We present a new technique for assisting users in classifying error reports. Our technique computes small, relevant queries presented to a user, which capture exact information that the analysis misses to either discharge or validate the error. In this paper, a methodology proper to detecting the security vulnerability is suggested by engrafting the symbol-based engine into the secure coding. Also, its effect was verified through the security vulnerability inspection test using the suggested symbolic execution engine. A notion of symbolically executing the program has been presented, which is closely related to the normal notion of program execution. It offers the advantage that one symbolic execution may represent a large, usually infinite, class of normal executions. This can be used for great advantages in the program inspecting and debugging.     

Ergonomic study of a vertical rope-pulling task from a scaffolding

The purpose of this study is to explore and assess manual material handling problems involving a vertical rope-pulling task from a scaffold (VRPS). Twenty-five young male Chinese subjects were recruited to participate in this study. The psychophysical method was used to investigate the effects of the rope material (nylon and hemp), rope diameter (6/8' and 4/8'), object size (bucket diameter 28 cm and 36 cm), operating with and without gloves on the maximum acceptable rope-pulling weight (MAWR), rating of perceived exertion (RPE) and heart rate, respectively. The results showed that the maximum acceptable rope-pulling weights were significantly affected by the rope material, rope diameter, object size and wearing or not wearing gloves. The MAWR for the hemp rope, coarse rope, small object size and without gloves was significantly greater than that for the nylon rope, fine rope, large object size and with gloves, respectively. However, the effect of the rope material, rope diameter, object size and with and without gloves on heart rate was not significant. The mean RPE response was significantly influenced by the rope material, object size and wearing or not wearing gloves. The most stressed body parts were the arms, fingers and wrists. The interaction effect between the rope material and wearing or not wearing gloves was significant. Generally, the VRPS for workers using hemp rope without gloves or using nylon rope without gloves was better than that for the other combinations.     

Digital placement system of a lateral tensioning rod during the formation of flexible-oriented three-dimensional composite preforms

The placement of a lateral tensioning rod is the main event during the formation of a flexible-oriented three-dimensional composite preform. Inaccurate placement can result in defective fiber weaving, degrading the overall quality of the preform. Furthermore, the manual execution of this task is time-consuming and ineffective. In this study, an automatic and digital placement method and mechanism are proposed to address these problems for the first time. In particular, an image processing method is proposed to recognize the placement region, where the geometric features are irregular and the feature differences are neglectable compared to the background. The placement position is then detected using a single industrial endoscope and the similar decision theorem of triangles. Furthermore, the proposed method and mechanism are applied to a digital placement system. The lateral tensioning rod can be accurately placed, as the relative error between the actual and ideal placement positions is within 0.5 %. The proposed method, therefore, automatizes the manual task and is essential in the development of preform formation toward digital manufacturing.     

基于工作流技术的RFID读取错误检测方法

针对工作流与射频识别(RFID)技术结合的系统中,RFID识别器对物件读取错误而造成工作流后续任务执行错误的问题,提出一种从工作流端理论模型出发进行RFID读取错误检测的方法。首先构建基于业务背景的工作流资源变化的流程模型即工作流资源变化模型(WRCM),结合贝叶斯网方法对模型进行资源的数据预测。将预测所得数据与实际RFID探测数据进行对比,分析得出出现读取失误的RIFD识别器。实验表明,工作流RFID结合的系统中,该方法能够一定程度上检测出存在读取错误的RFID识别器。     

Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill

We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models’ skill estimates were significant predictors of the two types of inquiry transfer tests.     

Satisfying agent goals by executing different task semantics: HTN, OWL-S or plug one yourself

In this paper, we propose a plan execution architecture which supports different task semantics. This way, each goal that is identified during the deliberation cycle of an agent can be satisfied through tasks defined in different semantics. The capability of supporting different task semantics provides two main advantages. The first advantage is the reuse of legacy artifacts within agent plans. The second advantage is simplifying the adaptation of an agent architecture to different standards of a business organization. In order to integrate various task semantics within a plan execution architecture, we have used a smoothly revised version of a previously articulated workflow model into which different task semantics would be reduced before execution. We have integrated hierarchical task network and OWL-S semantics into our plan execution architecture to test the strength of it in terms of support for executing different task semantics in an agent architecture. We think that such a plan execution architecture will contribute to the industrialization of agent architectures.     

Manipulating Deformable Linear Objects: Fuzzy-Based Active Vibration Damping Skill

Human can handle a deformable object and damp its vibration with recognized skill. However, for an industrial robot, handling a deformable object with acute vibration is often a difficult task. This paper addresses the problem of active damping skill for handling deformable linear objects (DLOs) by using a strategy inspired from human manipulation skills. The strategy is illustrated by several rules, which are explained by a fuzzy and a P controller. A proportional-integral-derivative (PID) controller is also employed to explain the rules as a comparison. The interpretations from controllers are translated into high level commands in a robotic language V+. A standard industrial robot with a force/torque sensor mounted on the wrist was employed to demonstrate the skill. Experimental results showed the fuzzy based damping skill is quite effective and stable even without any previous acknowledge of the deformable linear objects.Category (5)     

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before.Given a task and a graph of experts where each expert possesses some skills,we tend to find an effective team of experts who are able to accomplish the task.This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is,considering each expert has the minimum required level of skill.Here,we generalize the problem in multiple perspectives.First,a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors.Second,the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them.By considering the groups,search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members.Third,the existing RarestFirst algorithm is extended to more generalized version,and finally the cost definition is customized to improve the efficiency of selected team.Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness,our proposed framework is achieved well in practice.