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.

A Facial Expression Emotion Recognition Based Human-robot Interaction System

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

Interpreting and Extracting Open Knowledge for Human-Robot Interaction

A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

Augmented Virtual Stiffness Rendering of a Cable-driven SEA for Human-Robot Interaction

Human-robot interaction(HRI) is fundamental for human-centered robotics, and has been attracting intensive research for more than a decade. The series elastic actuator(SEA) provides inherent compliance, safety and further benefits for HRI, but the introduced elastic element also brings control difficulties. In this paper, we address the stiffness rendering problem for a cable-driven SEA system, to achieve either low stiffness for good transparency or high stiffness bigger than the physical spring constant, and to assess the rendering accuracy with quantified metrics. By taking a velocity-sourced model of the motor, a cascaded velocity-torque-impedance control structure is established. To achieve high fidelity torque control, the 2-DOF(degree of freedom) stabilizing control method together with a compensator has been used to handle the competing requirements on tracking performance, noise and disturbance rejection,and energy optimization in the cable-driven SEA system. The conventional passivity requirement for HRI usually leads to a conservative design of the impedance controller, and the rendered stiffness cannot go higher than the physical spring constant. By adding a phase-lead compensator into the impedance controller,the stiffness rendering capability was augmented with guaranteed relaxed passivity. Extensive simulations and experiments have been performed, and the virtual stiffness has been rendered in the extended range of 0.1 to 2.0 times of the physical spring constant with guaranteed relaxed passivity for physical humanrobot interaction below 5 Hz. Quantified metrics also verified good rendering accuracy.

Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.

Cyber-Physical-Social System Between a Humanoid Robot and a Virtual Human Through a Shared Platform for Adaptive Agent Ecology

Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.

A Survey of Human-centered Intelligent Robots:Issues and Challenges

Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

Robotic Etiquette:Socially Acceptable Navigation of Service Robots with Human Motion Pattern Learning and Prediction

Nonverbal and noncontact behaviors play a significant role in allowing service robots to structure their interactions withhumans.In this paper, a novel human-mimic mechanism of robot’s navigational skills was proposed for developing sociallyacceptable robotic etiquette.Based on the sociological and physiological concerns of interpersonal interactions in movement,several criteria in navigation were represented by constraints and incorporated into a unified probabilistic cost grid for safemotion planning and control, followed by an emphasis on the prediction of the human’s movement for adjusting the robot’spre-collision navigational strategy.The human motion prediction utilizes a clustering-based algorithm for modeling humans’indoor motion patterns as well as the combination of the long-term and short-term tendency prediction that takes into accountthe uncertainties of both velocity and heading direction.Both simulation and real-world experiments verified the effectivenessand reliability of the method to ensure human’s safety and comfort in navigation.A statistical user trials study was also given tovalidate the users’favorable views of the human-friendly navigational behavior.

Design of Two-wheeled Mobile Control Robot with Holographic Projection

In this paper, we design a two-wheeled mobile robot which could be control by Android mobile phone. The way that controlling robot through Android software is easy and convenient for people to use; while the two-wheeled mobile robot owns the characteristics of adaptation and flexibility. As a platform, this robot can accomplish a series of functions by combining it with different additional modules. Therefore, this robot has significant spreading value and promising application future. As for the principle of controlling the robot: by adjusting the direction of rotation of two isolating electric motor, we can keep its balance. By changing the dip of the robot, in other words, changing the speed of rotation of electric motor, we can determine the speed of the robot. Through controlling the difference of the speed between two electric motors, we can adjust the direction of the robot. All controlling depends on the feedback provided by optical encoder, gyroscope and accelerometer. As for human-computer interaction, we accomplish human-computer communication on the basis of Bluetooth, and we design an Android software used for direct the robot. Through experiment, we confirmed that the robot can keep balance itself, and can be controlled to move in a specific direction by our Android software. In the end, through establishing small-scale holographic projection equipment on the robot, we discuss the prospect of it.

Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

人-机器人交互设计现状及趋势

搜集现有的仿人机器人产品以及人-机器人交互相关的文献,分析人-机器人交互设计的现状及趋势。从仿人机器人的定义和现有产品出发,进行了市场调研,对搜集到的仿人机器人进行了外观和应用场景的归纳分类;并且使用Citespace对国内相关文献进行了分析。通过对仿人机器人的外观和应用场景的分类归纳总结,发现小型的仿人机器人更多应用于教育与儿童娱乐方面,而机器人的尺寸越是接近人类,则越是从事与人类肢体相关的工作。国内对于人-机器人交互的研究总的呈上升趋势,不断有新的仿人机器人在市场推出,并且所搭载的功能越来越全面。所进行的人-机器人相关调研与分析能够为之后的仿人机器人交互设计提供一定的参考。

Novel AR-based interface for human-robot interaction and visualization

Intuitive and efficient interfaces for human- robot interaction （HRI） have been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities. This paper presents a novel augmented reality （AR） based interface to facilitate human-virtual robot interaction. A number of human-virtual robot interaction methods have been for- mulated and implemented with respect to the various types of operations needed in different robotic applications. A Euclidean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment. A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods, and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot. Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.

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.

物理性人-机器人交互研究与发展现状

介绍了物理性人-机器人交互的定义,对该过程可能对人造成的伤害进行了分析,给出了物理性人-机器人交互安全评价指标,并总结了目前该领域采用的安全策略。综述了国内外在物理性人-机器人交互方面的研究进展,在分析物理性人-机器人交互研究现状的基础上,阐述了该领域面临的问题和挑战。文章指出,目前物理性人-机器人交互伤害分析和安全评估研究主要集中在钝/锐碰撞造成的骨骼和软组织伤害上,缺乏对脏器、血管、神经等损伤的研究。安全策略主要方法有碰撞避免、包裹软弹性材料、关节柔顺设计等。通过对比各种安全策略的设计思想和组成方案,总结出了各种策略的优缺点和存在的主要问题。由于现有的研究表明单一的手段和策略无法保证人-机器人交互的安全性,提出将认知性人-机器人交互和物理性人-机器人交互相结合来增强后者的安全性将是未来发展的方向。

基于Win-ROS的公共服务机器人人机交互系统设计

针对公共服务环境中服务机器人的低智能化水平,人机交互(HMI)不佳、用户操作不便捷、无法满足动态环境下复杂导览任务等问题,本文开发了一套新的服务机器人系统。该系统借鉴机器人操作系统(ROS)通讯机制,分布式网络模型,基于Topic的异步数据流通信思想,使用. NET开发框架,在Windows环境下开发完成。同时在该系统平台上使用动态Web资源技术实现的友好操作界面,基于语音云服务的人机交互模块,实现预定用户专属服务的人脸识别模块以及结合LTL与A*实现动态环境下复杂任务需求的机器人最优路径规划和导航模块。实验结果表明,该服务机器人系统较好地实现了人机交互功能、提高了用户的可操作性、具有较高的智能化水平,能够完成复杂导览任务,有效地提高了服务机器人的总体性能。

人-服务机器人交互中自然语言理解研究

针对与服务机器人交互过程中不受限的自然语言指令,提出了一种基于语料库的最大熵分类及理解算法。首先,通过收集家庭服务领域内的机器人控制语料,设计了一套有效的机器人控制指令体系。然后,对语料库进行对应控制指令的类别标注,从而把自然语言指令理解问题转化为分类问题。其次,通过对库中文本特征的提取和附加权重,建立最大熵分类器求解分类问题。最后,通过在所建控制指令语料库上进行多重交叉训练测试,控制指令理解的准确率可以达到90%以上。

矢量场逐次迭代的人-机身体交流控制

为使人和机器人在身体交流中保持运动协调同步,提出了基于矢量场逐次迭代的人-机身体交流智能控制方法。对智能控制方法中具有任意目标轨迹的三维空间矢量场逐次迭代系统进行算法设计,使其输出的关节期望位移信号能与输入的扭矩信号保持同步,且同步强度要达到能实现参数可调的目的;分析了具备不同矢量场的两个逐次迭代系统的相互作用情况;以人和机器人身体交流的典型例子——下肢外骨骼助力机器人为对象,对该智能控制方法进行了应用仿真,并分析了关节扭矩成分对同步运动的影响。仿真结果表明该控制方法有效。

基于手势跟踪的人-机器人协作研究

针对人-机器人协作团队中的交互问题,本文通过对手部对象的建模,提出了一种基于均值变换的粒子滤波算法以实现手势的实时准确的跟踪;针对移动机器人运动控制特点,提出了一个简单而实用的基于手势跟踪的动态手势理解方法,并应用于一个实际的人-多机器人协作团队。实验表明通过人提供辅助性关键信息的方法,能够有效地提高了机器人团队的任务执行效率。

可靠主义与机器人的证言

当前,人类越来越依赖机器人和其他形式的人工智能来实现自身的信仰,知识的见证来源或工具性知识来源的问题逐渐受到关注,可靠主义视角下的机器人证言或知识来源的分析不仅有助于对基本知识来源分类的认识论问题进行澄清,而且也拓展了可靠主义理论对于机器人知识的理解,进一步推动了当代知识论研究的实践转向。通过对可靠主义的概念界定及其划界问题的探讨,从机器人“这么说”中获得的知识分类、机器人证言的确证性、机器人知识的工具性三个层面对相关文献进行梳理与分析,可以得出将机器人证言视为知识来源往往会产生重要的社会和法律后果。有鉴于此,拟证明至少有一些机器人能够做证,并对可靠主义划界问题的三种解决路径进行探究,包括认知主义的解决路径、负责任的解决路径、自然信任的解决路径,但这些路径最终都没有成功解决可靠主义的划界问题。同时,基于机器人欺骗的实证性案例研究,提出可靠主义划界问题的社会信任解决路径,认为尽管某些被设计成具有欺骗性机器人,但人类对话者应该将它们及其证言视为知识的确证来源之一,发展可靠的人机关系要在认识论上立足于能够反映当代技术与人类本真关系的信念视域,在方法论上对机器人知识采用价值多元的方法,这对于现在和未来动态且可持续的人机交互具有重要意义。