满足不同交互任务的人机共融系统设计

人与机器人共同协作的灵活生产模式已经成为工业成产的迫切需求,因此,近年来人机共融系统方面的研究受到了越来越多关注.设计并实现了一种满足不同交互任务的人机共融系统,人体动作的估计和机器人的交互控制是其中的关键技术.首先,提出了一种基于多相机和惯性测量单元信息融合的人体姿态解算方法,通过构造优化问题,融合多相机下的2D关节检测信息和所佩戴的惯性测量单元测量信息,对人体运动学姿态进行优化估计,改善了单一传感器下,姿态信息不全面以及对噪声敏感的问题,提升了姿态估计的准确度.其次,结合机器人的运动学特性和人机交互的特点,设计了基于目标点跟踪和模型预测控制的机器人控制策略,使得机器人能够通过调整控制参数,适应动态的环境和不同的交互需求,同时保证机器人和操作人员的安全.最后,进行了动作跟随、物品传递、主动避障等人机交互实验,实验结果表明了所设计的机器人交互系统在人机共融环境下的有效性和可靠性.     

A new method to evaluate human-robot system performance

One of the key issues in space exploration is that of deciding what space tasks are best done with humans, with robots, or a suitable combination of each. In general, human and robot skills are complementary. Humans provide as yet unmatched capabilities to perceive, think, and act when faced with anomalies and unforeseen events, but there can be huge potential risks to human safety in getting these benefits. Robots provide complementary skills in being able to work in extremely risky environments, but their ability to perceive, think, and act by themselves is currently not error-free, although these capabilities are continually improving with the emergence of new technologies. Substantial past experience validates these generally qualitative notions. However, there is a need for more rigorously systematic evaluation of human and robot roles, in order to optimize the design and performance of human-robot system architectures using well-defined performance evaluation metrics. This article summarizes a new analytical method to conduct such quantitative evaluations. While the article focuses on evaluating human-robot systems, the method is generally applicable to a much broader class of systems whose performance needs to be evaluated.     

Human-Robot Interaction Through Gesture-Free Spoken Dialogue

We present an approach to human-robot interaction through gesture-free spoken dialogue. Our approach is based on passive knowledge rarefication through goal disambiguation, a technique that allows a human operator to collaborate with a mobile robot on various tasks through spoken dialogue without making bodily gestures. A key assumption underlying our approach is that the operator and the robot share a common set of goals. Another key idea is that language, vision, and action share common memory structures.We discuss how our approach achieves four types of human-robot interaction: command, goal disambiguation, introspection, and instruction-based learning. We describe the system we developed to implement our approach and present experimental results.     

Socially Distributed Perception: GRACE plays social tag at AAAI 2005

This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. Socially distributed perception is defined as a robot's ability to augment its limited sensory capacities through social interaction. We describe the task of social tag and its implementation on the robot GRACE for the AAAI 2005 Mobile Robot Competition & Exhibition. We then discuss our observations and analyses of GRACE's performance as a situated interaction with conference participants. Our results suggest we were successful in promoting a form of social interaction that allowed people to help the robot achieve its goal. Furthermore, we found that different social uses of the physical space had an effect on the nature of the interaction. Finally, we discuss the implications of this design approach for effective and compelling human-robot interaction, considering its relationship to concepts such as dependency, mixed initiative, and socially distributed cognition. An erratum to this article can be found at

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一种基于三维建图和虚拟现实的人机交互系统

以提高人机共融水平为目的,以救援机器人为背景,提出并实现基于三维建图和虚拟现实(VR)技术的人机交互系统.在该系统中,救援机器人基于多线激光雷达和惯性测量单元(IMU)实时构建环境的三维点云地图,并将建图结果增量式地表示为3D-NDT地图,实时传输至操作台的虚拟现实系统中可视化;同时,操作人员利用虚拟现实系统的交互设备生成机器人的控制指令,控制机器人运动,构成一个完整的人在回路的人机交互系统.该系统在将机器人环境实时在虚拟现实中可视化的基础上,可以给操作人员以极强的沉浸感,有利于操作人员更直接地理解机器人所处环境.此外,该系统作为一种新的人机交互方式,为提高人与机器人的自然交互水平提供了新思路,对促进人机交互技术的发展具有重要意义.     

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.      

An application of passive human-robot interaction: human tracking based on attention distraction

In this research, a taxonomy is introduced to cover important considerations for human-robot interactions. As an application of passive human-robot interaction, two modalities for localizing humans based on sound source localization and infrared motion detection were developed and integrated with the face-tracker system of a humanoid ISAC (intelligent soft arm control), in order to direct ISACs attention and to prevent it from being quickly distracted. The sound source localization and passive infrared motion detection systems are used to provide the face-tracker system with candidate regions for finding a face. In order to avoid the situation where the robot appears to be "hyperactive" and cannot give sufficient attention to a newly discovered face, these sensing modules should not directly gain control of the tracking if the system has recently acquired a new face. Our goal is to allow a human to redirect the attention of the system but give the system a method to ignore the distraction if recently engaged.     

机器人的CP-nets优化类人轨迹规划

机器人移动轨迹按照人的手臂来模拟是提高机器人安全性和人机交互能力的有效方法,特别是针对机器人抓取路径不唯一的场合,类人行为对于人机系统表现更加自然。此前,通常利用Kinect等设备,基于人工神经网络和K近邻算法等智能算法对类人轨迹进行规划,但无法获得未采样过的最优轨迹。本文基于CP-nets采用偏好模型研究类人运动轨迹,然后将该模型应用于机器人控制,在没有采样的情况下,也可得到最优的类人轨迹。实验结果表明,基于CP-nets 的类人规划轨迹具有较高的效率和舒适性,符合人的运动特征。     

基于QFD与RAHP的餐馆服务机器人 人因工程设计

摘 要：以用户需求为基础,提出一种新型餐馆服务机器人人机系统(HRS)模式,探讨了基 于质量功能展开(QFD)理论的餐馆服务机器人人因工程(HRE)设计方法。运用抽样调查法、问卷 调查法和亲和图法(KJ)获取关于餐馆服务机器人用户需求的层次化模型。引入粗糙层次分析法 (RAHP)计算各需求特征所占权重,以此分析基于 QFD 的餐馆服务机器人 HRE 设计方法中的重 点设计目标。将用户重点需求转化为设计要素,设定功能、外观及人机的详细质量特征,通过 构建质量屋对质量功能展开研究,提出设计方案,最终运用 CATIA 人机分析软件对设计方案进 行可用性评价及验证可行性。研究表明该设计方法及流程可提升餐馆服务机器人的可用性,为 后续设计提供参考。     

Psychological Effects of Behavior Patterns of a Mobile Personal Robot

It is envisioned that in the near future personal mobile robots will be assisting people in their daily lives. An essential characteristic shaping the design of personal robots is the fact that they must be accepted by human users. This paper explores the interactions between humans and mobile personal robots, by focusing on the psychological effects of robot behavior patterns during task performance. These behaviors include the personal robot approaching a person, avoiding a person while passing, and performing non-interactive tasks in an environment populated with humans. The level of comfort the robot causes human subjects is analyzed according to the effects of robot speed, robot distance, and robot body design, as these parameters are varied in order to present a variety of behaviors to human subjects. The information gained from surveys taken by 40 human subjects can be used to obtain a better understanding of what characteristics make up personal robot behaviors that are most acceptable to the human users.     

Control sharing in human-robot team interaction

The interaction between humans and robot teams is highly relevant in many application domains, for example in collaborative manufacturing, search and rescue, and logistics. It is well-known that humans and robots have complementary capabilities: Humans are excellent in reasoning and planning in unstructured environments, while robots are very good in performing tasks repetitively and precisely. In consequence, one of the key research questions is how to combine human and robot team decision making and task execution capabilities in order to exploit their complementary skills. From a controls perspective this question boils down to how control should be shared among them. This article surveys advances in human-robot team interaction with special attention devoted to control sharing methodologies. Additionally, aspects affecting the control sharing design, such as human behavior modeling, level of autonomy and human-machine interfaces are identified. Open problems and future research directions towards joint decision making and task execution in human-robot teams are discussed.     

People detection through quantified fuzzy temporal rules

The knowledge about the position and movement of people is of great importance in mobile robotics for implementing tasks such as navigation, mapping, localization, or human-robot interaction. This knowledge enhances the robustness, reliability and performance of the robot control architecture. In this paper, a pattern classifier system for the detection of people using laser range finders data is presented. The approach is based on the quantified fuzzy temporal rules (QFTRs) knowledge representation and reasoning paradigm, that is able to analyze the spatio-temporal patterns that are associated to people. The pattern classifier system is a knowledge base made up of QFTRs that were learned with an evolutionary algorithm based on the cooperative-competitive approach together with token competition. A deep experimental study with a Pioneer II robot involving a five-fold cross-validation and several runs of the genetic algorithm has been done, showing a classification rate over 80%. Moreover, the characteristics of the tests represent complex and realistic conditions (people moving in groups, the robot moving in part of the experiments, and the existence of static and moving people).     

A software tool for human-robot shared-workspace collaboration with task precedence constraints

A key challenge in human-robot shared workspace is defining the decision criterion to select the next task for a fluent, efficient and safe collaboration. While working with robots in an industrial environment, tasks may comply with precedence constraints to be executed. A typical example of precedence constraint in industry occurs at an assembly station when the human cannot perform a task before the robot ends on its own. This paper presents a methodology based on the Maximum Entropy Inverse Optimal Control for the identification of a probability distribution over the human goals, packed into a software tool for human-robot shared-workspace collaboration. The software analyzes the human goal and the goal precedence constraints, and it is able to identify the best robot goal along with the relative motion plan. The approach used is, an algorithm for the management of goal precedence constraints and the Partially Observable Markov Decision Process (POMDP) for the selection of the next robot action. A comparison study with 15 participants was carried out in a real world assembly station. The experiment focused on evaluating the task fluency, the task efficiency and the human satisfaction. The presented model displayed reduction in robot idle time and increased human satisfaction.     

Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions (HRI) includes both:the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields. As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities:least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure. Finally, with the best fictitious force and its identified impedance, an impedance control is designed for a mobile robot Pioneer 3AT, which is programmed to follow a human in a structured scenario. According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.      

Control architecture for human-robot integration: application to a robotic wheelchair.

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human-robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human-robot-integration concept, the authors take a further step in the typical human-robot relation, since they consider her/him as a constituent part of the human-robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human-robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human-robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people.     

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.     

CNN实现的运动想象脑电分类及人-机器人交互

基于脑电的脑机交互能帮助肢体运动障碍患者进行日常生活和康复训练,但是,由于脑电信号存在信噪比较低、个体差异性大等问题,导致脑电特征的提取与分类还需要进一步提高准确性和效率.因此,在减少脑电采集通道数目、增加分类数目的前提下,基于卷积神经网络对运动想象中的脑电信号进行分类.首先,基于已有方法进行探索实验,建立由3层卷积层、3层池化层和2层全连接层构成的卷积神经网络;然后针对想象左手、右手、脚的运动和静息态设计与开展了实验,获取了相关脑电数据;之后,利用脑电数据训练出基于卷积神经网络的分类模型,测试结果表明,该模型平均分类识别率达到了82.81%,且高于已有的相关分类算法;最后,将已建立的分类模型应用于运动想象信号的在线分类,设计与开发了脑机交互应用原型系统,驱动人-机器人之间的实时交互,帮助用户利用运动想象控制仿人机器人的抬手、前进等运动状态.进一步的测试结果表明,机器人对用户控制命令的平均识别率达到了80.31%,从而验证了所提方法可以对运动想象脑电数据进行较为精确的实时分类,可以促进脑机接口技术在人-机器人交互中的应用.     

基于GPR和深度强化学习的分层人机协作控制

提出了一种基于高斯过程回归与深度强化学习的分层人机协作控制方法,并以人机协作控制球杆系统为例检验该方法的高效性.主要贡献是:1)在模型未知的情况下,采用深度强化学习算法设计了一种有效的非线性次优控制策略,并将其作为顶层期望控制策略以引导分层人机协作控制过程,解决了传统控制方法无法直接应用于模型未知人机协作场景的问题; 2)针对分层人机协作过程中人未知和随机控制策略带来的不利影响,采用高斯过程回归拟合人体控制策略以建立机器人对人控制行为的认知模型,在减弱该不利影响的同时提升机器人在协作过程中的主动性,从而进一步提升协作效率; 3)利用所得认知模型和期望控制策略设计机器人末端速度的控制律,并通过实验对比验证了所提方法的有效性.