预测行人运动的服务机器人POMDP导航

为提高室内动态环境下服务机器人对行人的自然避让能力,对人的运动轨迹模式进行建模,在此基础上引入了将行人运动长、短期预测结合起来的方法．为适应传感器噪声及网络延迟等因素所造成的感知—控制回路中的多源不确定性,将人与机器人的相对位置关系建模为部分可观的马尔可夫状态．采用部分可观的马尔可夫决策过程（POMDP）进行多源不确定性下的概率决策,协调控制机器人全局路径规划、反应式运动及速度控制等行为模块．实验结果验证,它能够实现提前避碰的安全导航,因避免反复的曲折与徘徊运动而提高了机器人导航效率．     

Machine Understanding of Human Behavior in Human Computer Interaction

Human computer interaction (HCI)is one of the foremost challenges of our society.New paradigms for interacting with computers will define the 21st century and enable the world to communicate and interact effortlessly and intuitively.A widely accepted prediction is that the     

Machine Understanding of Human Behavior in Human Computer Interaction

Human computer interaction(HCI)is one of the foremost challenges of our society.New paradigms for interacting with computers will define the 21st century and enable the world to communicate and interact effortlessly     

A Mode-Switching Motion Control System for Reactive Interaction and Surface Following Using Industrial Robots

This work proposes a sensor-based control system for fully automated object detection and exploration (surface following) with a redundant industrial robot. The control system utilizes both offline and online trajectory planning for reactive interaction with objects of different shapes and color using RGBD vision and proximity/contact sensors feedback where no prior knowledge of the objects is available. The RGB-D sensor is used to collect raw 3D information of the environment. The data is then processed to segment an object of interest in the scene. In order to completely explore the object, a coverage path planning technique is proposed using a dynamic 3D occupancy grid method to generate a primary (offline) trajectory. However, RGB-D sensors are very sensitive to lighting and provide only limited accuracy on the depth measurements. Therefore, the coverage path planning is then further assisted by a real-time adaptive path planning using a fuzzy self-tuning proportional integral derivative (PID) controller. The latter allows the robot to dynamically update the 3D model by a specially designed instrumented compliant wrist and adapt to the surfaces it approaches or touches. A modeswitching scheme is also proposed to efficiently integrate and smoothly switch between the interaction modes under certain conditions. Experimental results using a CRS-F3 manipulator equipped with a custom-built compliant wrist demonstrate the feasibility and performance of the proposed method.      

Agent动态交互信任预测与行为异常检测模型

在agent理论中,信任计算是一个有意义的研究方向.然而目前agent信任研究都是以平均交互成功率来计算,较少考虑信任动态变化,因而准确预测和行为异常检测的能力不能令人满意.针对上述问题,以概率论为工具,按时间分段交互历史给出agent交互信任计算模型CMAIT;结合信任的变化率,给出信任计算的置信度和异常检测机制.实验以网上电子商务为背景,实验结果显示该计算模型的预测误差为TRAVOS的0.5倍,计算量也较少;既可用于对手历史行为的异常检测,防止被欺骗,又可用于对手未来行为的预测.改进了Jennings等人关于agent信任的工作.     

Evaluating the Robot Personality and Verbal Behavior of Domestic Robots Using Video-Based Studies

Robots are increasingly being used in domestic environments and should be able to interact with inexperienced users. Human–human interaction and human–computer interaction research findings are relevant, but often limited because robots are different from both humans and computers. Therefore, new human–robot interaction (HRI) research methods can inform the design of robots suitable for inexperienced users. A video-based HRI (VHRI) methodology was here used to carry out a multi-national HRI user study for the prototype domestic robot BIRON (BIelefeld RObot companioN). Previously, the VHRI methodology was used in constrained HRI situations, while in this study HRIs involved a series of events as part of a 'hometour' scenario. Thus, the present work is the first study of this methodology in extended HRI contexts with a multi-national approach. Participants watched videos of the robot interacting with a human actor and rated two robot behaviors (Extrovert and Introvert). Participants' perceptions and ratings of the robot's behaviors differed with regard to both verbal interactions and person following by the robot. The study also confirms that the VHRI methodology provides a valuable means to obtain early user feedback, even before fully working prototypes are available. This can usefully guide the future design work on robots, and associated verbal and non-verbal behaviors.     

Human Detection and Identification by Robots Using Thermal and Visual Information in Domestic Environments

In this paper a robust system for enabling robots to detect and identify humans in domestic environments is proposed. Robust human detection is achieved through the use of thermal and visual information sources that are integrated to detect human-candidate objects, which are further processed in order to verify the presence of humans and their identity using face information in the thermal and visual spectrums. Face detection is used to verify the presence of humans, and face recognition to identify them. Active vision mechanisms are employed in order to improve the relative pose of a candidate object/person in case direct identification is not possible. The response of the different modules is characterized, and the proposed system is validated using image databases of real domestic environments, and human detection and identification benchmarks of the RoboCup@Home research community.     

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.     

机器人的人体姿态动作识别与模仿算法

提出从人体姿态估计的角度实现人与NAO机器人的互动交流。用AdaBoost算法训练得到人体部件检测器,利用得到的部件检测器在图片中寻找到对应部件的若干候选区域,在寻找的过程中兼顾部件的位置和方向。依据提出的评价指标评价这些候选区域,从中选出最优。以NAO机器人为实验平台,在其动作可达范围内实现对人体动作姿态的识别与模仿,达到较好的动作空间一致性和时间一致性。实验结果证明该算法可有效识别和模仿人体姿态。     

基于时域分割的人体行为连续性动作预测仿真

传统人体行为动作预测方法对人体骨架上的所有关键特征映射点包含范围模糊,导致实时处理速度慢,无法有效对连续性动作进行预测,且错位率较高.为此提出一种基于时域分割的人类行为连续性动作预测.首先通过三维空间捕捉技术构建人体骨架模型,通过时域分割确定关节点的位置信息,将任意关节点都视为映射特征点,提取人体的运动数据特征,利用L...     

The Prospects for Psychological Science in Human-Computer Interaction

This paper discusses the prospects of psychology playing a significant role in the progress of human-computer interaction. In any field, hard science (science that is mathematical or otherwise technical) has a tendency to drive out softer sciences, even if the softer sciences have important contributions to make. It is possible that, as computer science and artificial intelligence contributions to human-computer interaction mature, this could happen to psychology. It is suggested that this trend might be prevented by hardening the applicable psychological science. This approach, however, has been criticized on the grounds that the resulting body of knowledge would be too low level, too limited in scope, too late to affect computer technology, and too difficult to apply. The prospects for overcoming each of these obstacles are analyzed here.     

基于强化学习和模糊逻辑的移动机器人导航

自主导航是移动机器人的一项关键技术。该文采用强化学习结合模糊逻辑的方法实现了未知环境下自主式移动机机器人的导航控制。文中首先介绍了强化学习原理,然后设计了一种未知环境下机器人导航框架。该框架由避碰模块、寻找目标模块和行为选择模块组成。针对该框架,提出了一种基于强化学习和模糊逻辑的学习、规划算法：在对避碰和寻找目标行为进行独立学习后,利用超声波传感器得到的环境信息进行行为选择,使机器人在成功避碰的同时到达目标点。最后通过大量的仿真实验,证明了算法的有效性。     

移动机器人行为协同开发框架与应用研究

移动机器人在生产生活当中日益发挥重要作用，但移动机器人开发应用却比较困难，主要存在两个问题：一是软件模块化和重用性，二是行为协同控制模型的理论支撑。针对上述问题，融合了已在学术界和工业界取得广泛应用的开源机器人软件平台--机器人操作系统（Robot Operaing System，ROS），以及多主体理论中，支撑主体内部行为协同机制的反应式包容模型（Subsumption Architecture），设计并实现了一个移动机器人行为协同开发框架。基于机器人操作系统底层机制，把包容模型的基本控制元件封装成API，在功能单元内部提供基于有限状态机的开发模板，在功能单元之间提供松散耦合的通信机制，并且提供了基于框架的应用开发的模型设计方法学和应用实现方法学。用户使用本文框架开发移动机器人，因为框架把包容模型中的关键控制元件封装成API，使得用户不用了解复杂的控制模型理论以及底层模块执行机制和通信机制，并且可以重用机器人操作系统提供的开源软件包，几乎没有额外的学习代价，十分地方便高效。通过框架研究并且开发了具有三个行为层的自主漫游机器人以及具有六个行为层的安保巡逻机器人，在仿真器和实际场景中设置不同地图环境，进行机器人行为实验，通过实验结果分析，进一步验证了框架的易用性、可靠性和鲁棒性。     

计算机视觉在球类机器人和行为理解研究中的应用与发展

智能机器人是当今机器人研究的热点，并逐渐由制造领域向非制造领域发展。本文以几种国内外近年研制的新型球类运动机器人为例，介绍了计算机视觉在智能机器人研究中的应用情况。另外，有关人与机器人之间的视觉行为理解研究是计算机视觉研究与智能机器人研究领域的新发展，本文对此也进行了介绍。     

基于GoGCN的软件系统类交互关系预测

软件系统是一个复杂的人工制品,类之间的交互关系对软件质量有着潜在影响,如软件缺陷的级联传播效应就是一个典型.如何准确预测软件系统中类之间合理关系,优化设计结构是软件质量保障的一个开放问题.从软件网络观的视角,综合考虑软件系统中类与类之间关系(外部图),以及每个类内部方法之间关系(内部图),将软件系统抽象成一个图中图结构的软件网络,并在此基础上提出一种基于图中图卷积神经网络的类交互关系预测方法.首先对每个类内部图进行卷积得到类节点的初始特征,再通过外部图的卷积更新类节点的表征向量,最后通过计算类节点对的评估值进行交互预测.根据在6个Java开源项目上的实验结果显示,图中图结构有助于提高软件系统结构的表征能力,且所提方法与常规网络嵌入方法相比, AUC值和AP值的平均增长率超过5.5%.与此同时,和两种同行方法相比, AUC值和AP值的平均增长率分别在9.36%和5.22%以上.     

The Epigenesis of Meaning in Human Beings,and Possibly in Robots

Minds and Machines - This article addresses a classical question: Can a machine use language meaningfully and if so, how can this be achieved? The first part of the paper is mainly philosophical....     

面向仿人机器人自然步态规划的人体步行实验分析

自然步态规划方法是实现仿人机器人步态柔顺和能量优化的可行方法,该方法要求对人体步行及其平衡策略进行定量研究．本文分析自然步态规划方法的原理,建立了一套快捷有效的人体步态测试系统,并通过实验建立了人体步行的参数化数据库．实验结果揭示了人体步行的参数化特征及其平衡策略,对于仿人机器人的自然步态规划及控制提供了理论指导．结论特别指出,仅仅通过规划的方式实现仿人机器人的自然步态是不完备的,自然步态的实现必须同仿生控制策略相结合．同时实验结论对于仿人机器人的本体优化设计也提供了参考．     

Using Virtual Pheromones and Cameras for Dispersing a Team of Multiple Miniature Robots

To safely and efficiently guide personnel of search and rescue operations in disaster areas, swift gathering of relevant information such as the locations of victims, must occur. Using the concept of ‘repellent virtual pheromones’ inspired by insect colony coordination behaviors, miniature robots can be quickly dispersed to survey a disaster site. Assisted by visual servoing, dispersion of the miniature robots can quickly cover an area. An external observer such as another robot or an overhead camera is brought into the control loop to provide each miniature robot estimations of the positions of all of the other near-by robots in the robotic team. These miniature robots can then move away from the other near-by robots on the team, resulting in the robot collective becoming swiftly distributed through the local area. The technique has been simulated with differing pheromone persistence levels and implemented using the miniature Scout robots, developed by the Center for Distributed Robotics at the University of Minnesota, which are well-suited to surveillance and reconnaissance missions.     

Efficient and Precise Cache Behavior Prediction for Real-Time Systems

Abstract interpretation is a technique for the static detection of dynamic properties of programs. It is semantics based, that is, it computes approximative properties of the semantics of programs. On this basis, it supports correctness proofs of analyses. It replaces commonly used ad hoc techniques by systematic, provable ones, and it allows for the automatic generation of analyzers from specifications by existing tools. In this work, abstract interpretation is applied to the problem of predicting the cache behavior of programs. Abstract semantics of machine programs are defined which determine the contents of caches. For interprocedural analysis, existing methods are examined and a new approach that is especially tailored for the cache analysis is presented. This allows for a static classification of the cache behavior of memory references of programs. The calculated information can be used to improve worst case execution time estimations. It is possible to analyze instruction, data, and combined instruction/data caches for common (re)placement and write strategies. Experimental results are presented that demonstrate the applicability of the analyses.     

Integration Strategies Using a Modular Architecture for Mobile Robots in the Rehabilitation Field

This paper describes an integration strategy based upon a modular architecture which is meant to improve access to assistive technical devices in the rehabilitation field. This system concept is now known as M3S: Multiple Master Multiple Slave. With M3S, it is possible to connect input devices (like joysticks and keyboards) to end-effectors (like wheelchairs, robots and infra-red remote controllers) to form an integral aid which offers disabled people better opportunities to function as independently as possible. Since M3S is based upon a modular architecture, it allows users (disabled people, attendants, therapists) to compile a specific package of any combination of technical aids to a complete integral system, while still permitting them to extend or modify the system later on. Furthermore the system can be used right-away without any special adaptations using the M3S plug-and-play capabilities. The power of such an integrated system have been shown in several user evaluations in various countries around Europe. The M3S specification is an open standard available for free, M3S has also been proposed to the ISO for formal standardization. For the development of M3S devices a complete set of software tools is available at no cost, hardware starter kits are available for a small fee. Information about M3S can be acquired from the M3S web server (http://www.tno.nl/m3s) or directly from the M3S Dissemination office.