简化路况模式下驾驶员情绪模型的研究

驾驶辅助系统中的驾驶员模型较为单一, 没有考虑驾驶员的情绪状态对驾驶策略的影响. 为此, 本文研究了简化路况下驾驶员的情绪模型. 基于OCC (Ortony-clore-collins) 模型、情绪状态自发转移过程的马尔科夫模型和情绪状态刺激转移的隐马尔科夫模型(Hidden Markov model, HMM), 本文提出路况变化和无路况两种情况下的情绪模型, 并对驾驶员的跟驰、切换车道和超车过程中的情绪变化进行了研究. 在自发转移过程中, 结合情绪实时变化的特性, 提出了时变的自发转移过程,而在情绪刺激转移中, 考虑了情感对刺激的记忆效应, 即同种刺激先后对情感影响不同. 讨论了认知情感的变化对驾驶策略的影响. 针对车距、路宽和周围车辆车速对驾驶员的情感影响程度、刺激敏感程度以及特定事件对驾驶员的影响过程, 进行了仿真实验, 预估出驾驶员在特定事件刺激下会采取何种驾驶策略. 并进行了实测数据验证, 实验结果验证了所提出模型的有效性, 为驾驶辅助系统中建立驾驶员模型提供了有借鉴意义的基础理论.     

基于心理能量思想的人工情感模型

从人工情感建模的需要出发,根据动力心理学关于心理能量的理论,提出了情感能量的概念以及基于情感能量的情感状态的数学描述方法,建立了情感状态的能量分布描述空间和情感状态的概率描述空间。在此基础上,进一步分析了情绪状态的变化过程,并提出了情绪状态自发转移过程的马尔可夫链模型以及情绪状态刺激转移过程的隐马尔可夫模型。     

家庭服务机器人情感计算模型的研究

根据基本情感理论建立了家庭服务机器人的情感状态概率空间模型,并应用马尔可夫链的特性,建立了基于隐马尔可夫模型的情感计算模型.详细地阐述了该情感计算模型中各参数的意义以及估算方法.通过仿真实验验证了该情感计算模型可以较好地模拟情感状态的自发转移,以及在外部刺激作用下的情感转移.通过对实验数据分析,发现机器人的情感经外部刺激作用或者自发演变,最终趋于稳定状态,这个稳定状态与情感转移概率矩阵有关,而与机器人所处的初始情感状态无关.     

人工心理情感模型的个人机器人

以心理动力学中心理能量概念为基础,根据情感能量守恒定律,建立了情感状态能量分布描述空间和情感状态的概率描述空间,分析情绪状态的变化过程,提出情绪状态自发转移过程的隐马尔可夫链及其模型算法.利用MATLAB建立相关情感状态变化的仿真研究平台,研究情感状态的变化规律.根据以上人工心理情感模型及其变化规律构建出个人机器人综合研究平台软、硬件体系结构,并通过该系统的实际运行实验验证了其有效性.     

情感机器人的情感模型研究

本文建立了仿人类情感的情感模型,在情感模型中建立了三维情感空间,采用马尔可夫过程来描述情感状态的变化转移过程,并提出了性格矩阵,对比分析了不同性格人对相同刺激的反应.应用D-S证据理论融合来自视觉、声音及其他方面的情感信息,分析两种因素同时作用时引起情感状态的转移规律.最后将情感模型应用于情感机器人系统,使机器人可以根据外界刺激产生情感,并做出相应的表情.实验结果证明了情感模型的有效性.     

自适应性呼叫中心

重点介绍自适应性应答及路由机制的设计与实现.自适应性应答及路由机制在呼叫系统中用来管理整个呼叫流程.该机制将呼叫一开始就提供给用户个性化的自动语音应答环境与后续的自适应性人工应答环境完美地组合在一个完整的工作流程中.自适应性应答及路由机制根据不同的主叫用户属性和话务员属性,将提供个性化的自动语音应答内容和最优的人工话务员应答选择,从而使整个呼叫应答工作流程更为人性化.在实现的机制方面,通过结合两种适应性模型:用户模型和域模型,提供给用户一个高度个性化的应答交互体验.     

表情机器人的动态情绪调节过程研究

本文提出了一种基于概率有限状态机的表情机器人情绪表现模型,将其应用到实时动态调节的表情机器人面部表情上．为实现该模型,首先定义表情机器人的情绪状态空间,并通过调查获取不同情绪状态的刺激转移概率．结合Gross的情绪调节过程,抽象出情绪表现规则中的抑制特征因子和人机交互关系因子,并使用遗传算法对其进行优化,同时采用自适应变异概率算子和交叉算子对优化过程进行实时的调节,其参数性能得到了相应的提高．对模型参数进行了量化研究及交互效果的仿真分析,并在所研制的23自由度表情机器人平台上进行了相关实验．此外,对于实际交互效果,还进行了统计学的调查分析．结果表明,本模型能够摆脱单一的表情交流方式,得到符合当前交互环境的表情．     

情绪建模与情感虚拟人研究

以人工心理和基本的情绪理论为基础，在三维情绪空间中将个性和OCC模型相结合，建立了一个情绪模型。并将此模型作为情感核心，采用VB和Viavoice、科大讯飞以及TalkingShow应用软件尝试实现了一个情感虚拟人。这个情感虚拟人不仅具有学习、记忆能力，而且具有情感交互能力。     

基于情感-时空信息的机器人服务自主认知及个性化选择

为了提高机器人的人机交互能力,针对家庭服务机器人在认知服务任务时往往忽略用户情感因素的弊端,提出了以用户情感为核心的机器人服务任务自主认知方法以及个性化服务选择策略.首先,利用智能空间本体技术结合用户情感状态与时间空间信息建立情感-时空本体模型,消除智能空间中的信息异构性.在此基础上,将与情感-时空相关的服务规则库编码并训练BP（逆向传播）神经网络构建推理机,将实时更新的智能空间信息与神经网络相匹配,推理出机器人需要执行的服务,实现机器人对以用户情感为核心的服务任务自主认知.最后,将用户情感状态作为执行服务的奖惩反馈信号,对服务集合中的子类服务进行动态的偏好度调节,完成有针对性的服务选择.仿真结果表明,基于该方法能够实现以用户情感为核心的机器人服务任务自主认知,同时可以根据用户偏好变化提供个性化的服务,有效提高了家庭服务机器人的智能性和灵活性,增强了用户的服务体验.     

基于状态空间与概率空间映射的极大相似度匹配情感模型

机器人情感建模是研究情感机器人的热点问题。文中以情感心理学知识为基础,模拟具有不同个性的情感机器人在外界刺激作用下情感动态变化的过程,研究个性和外界刺激对情感转移过程的影响。采用基于状态空间的情感空间模型来描述机器人的情感状态,并用HMM过程来模拟情感状态的转移过程。但HMM过程只能求得当前情感状态的概率,为得到具体的情感状态,文中提出一种基于状态空间与概率空间映射的极大相似度匹配的情感转移模型。首先利用HMM过程计算出当前情感概率,然后通过极大相似度匹配来得到转移后具体的情感状态。通过调节模型参数来模拟不同个性和外界刺激,该模型能有效模拟情感状态变化过程。实验结果验证模型模拟的情感变化过程符合人类情感变化的一般规律。     

A two-dimensional facial-affect estimation system for human–robot interaction using facial expression parameters

A social robot should be able to autonomously interpret human affect and adapt its behavior accordingly in order for successful social human–robot interaction to take place. This paper presents a modular non-contact automated affect-estimation system that employs support vector regression over a set of novel facial expression parameters to estimate a person’s affective states using a valence-arousal two-dimensional model of affect. The proposed system captures complex and ambiguous emotions that are prevalent in real-world scenarios by utilizing a continuous two-dimensional model, rather than a traditional discrete categorical model for affect. As the goal is to incorporate this recognition system in robots, real-time estimation of spontaneous natural facial expressions in response to environmental and interactive stimuli is an objective. The proposed system can be combined with affect detection techniques using other modes, such as speech, body language and/or physiological signals, etc., in order to develop an accurate multi-modal affect estimation system for social HRI applications. Experiments presented herein demonstrate the system’s ability to successfully estimate the affect of a diverse group of unknown individuals exhibiting spontaneous natural facial expressions.     

情感计算数学模型的研究初探

针对计算机如何进行情感计算，提出一种情感空间的概率模型并对其进行了计算机仿真。通过构造状态的概率转移矩阵，得到每个情感状态的概率分布，从而计算出情感的熵值。情感的熵值，表达出所构造的情感的细腻程度。通过计算机仿真，验证了情感模型是符合人类情感规律的——不确定性同时又具有统计规律。最后通过不同参数的仿真，说明此模型可以产生不同的情感活动，为情感机器人数学模型的建立与情感决策支持系统提供了一种新的方法。     

Gesture Spotting and Recognition for Human–Robot Interaction

Visual interpretation of gestures can be useful in accomplishing natural human-robot interaction (HRI). Previous HRI research focused on issues such as hand gestures, sign language, and command gesture recognition. Automatic recognition of whole-body gestures is required in order for HRI to operate naturally. This presents a challenging problem, because describing and modeling meaningful gesture patterns from whole-body gestures is a complex task. This paper presents a new method for recognition of whole-body key gestures in HRI. A human subject is first described by a set of features, encoding the angular relationship between a dozen body parts in 3-D. A feature vector is then mapped to a codeword of hidden Markov models. In order to spot key gestures accurately, a sophisticated method of designing a transition gesture model is proposed. To reduce the states of the transition gesture model, model reduction which merges similar states based on data-dependent statistics and relative entropy is used. The experimental results demonstrate that the proposed method can be efficient and effective in HRI, for automatic recognition of whole-body key gestures from motion sequences     

基于BBN情感模型的和谐人机交互研究

提出了一种将个性、情感、情绪分层表示的思想,并且利用贝叶斯网络进行情感建模,通过虚拟人脸的面部表情来反映情绪的变化,旨在赋予机器类人的情感,达到更加真实和谐的人机交互。最后将此情感模型应用于情感虚拟人交互系统,实验证明,该模型简单、稳定,且易于实现。     

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.     

Optimization of criterion for objective evaluation of HRI performance that approximates subjective evaluation: a case study in robot competition

The standard approach to evaluate the performance of human-robot interaction (HRI) is subjective evaluation, for example, using questionnaires. Because such subjective evaluation is time-consuming, an alternative evaluation method based on only objective factors (i.e. human reaction behavior) is required for autonomous learning by robots and for scoring in robot competitions, etc. To this end, we aimed to investigate the extent to which subjective evaluation results can be approximated using objective factors. We observed and stored HRI history data through a robot-competition task in which the robot was required to generate comprehensible and unambiguous natural language expressions and gestures to guide inexpert users in virtual everyday environments. In addition, to acquire subjective evaluation results, we asked third-parties to evaluate the HRI performance by reviewing the stored interaction histories. From the results of a case study of robot-competition, we demonstrate the necessity for an objective method for the evaluation of HRI performance and a determination process for an effective evaluation criterion. The results of a multiple linear regression analysis to estimate the subjective evaluation results reveal that the subjective evaluation of HRI can indeed be reasonably approximated base on objective factors.     

Affective State Estimation for Human–Robot Interaction

In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine. [All rights reserved Elsevier].     

基于情绪心理学的情感建模

基于情绪心理学的基本理论，定义了个性空间、情感空间和心情空间，建立了个性与心情、心情与情感的映射关系，给出了心情与情感状态更新方程，提出了一种能够合理反映人类情感变化规律的情感计算模型。实验表明，该模型能合理反映出在外界刺激作用下，不同个性者心情状态和情感状态的波动过程，为情感机器人的情感决策提供了一种新的机制。