Alle Publikationen
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2017
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(2017) : Socially-aware navigation planner using models of human-human interaction: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Lisbon, Portugal: IEEE Robotics & Automation Society, S. 405-410
DOI: https://doi.org/10.1109/ROMAN.2017.8172334 Abstract: In this paper, we revisit a real-time socially-aware navigation planner which helps a mobile robot to navigate alongside humans in a socially acceptable manner. This navigation planner is a modification of nav core package of Robot Operating System (ROS), based upon earlier work and further modified to use only egocentric sensors. The planner can be utilized to provide safe as well as socially appropriate robot navigation. Primitive features including interpersonal distance between the robot and an interaction partner and features of the environment (such as hallways detected in real-time) are used to reason about the current state of an interaction. Gaussian Mixture Models (GMM) are trained over these features from human-human interaction demonstrations of various interaction scenarios. This model is both used to discriminate different human actions related to their navigation behavior and to help in the trajectory selection process to provide a social-appropriateness score for a potential trajectory. This paper presents an evaluation done in simulation while utilizing data from real human interactions.
Keywords: Angemessen(heit) (von Technik), egocentric sensors, Feature extraction, Gaussian Mixture Models, Gaussian processes, human actions, human interactions, human-human interaction demonstrations, human-robot interaction, Humans, ieee xplore, interaction partner, interaction scenarios, mixture models, mobile robot, Mobile robots, nav core package, Navigation, navigation behavior, path planning, primitive features, Real-time systems, Robot Operating System, Robot sensing systems, service robot, social-appropriateness score, socially acceptable manner, socially appropriate robot navigation, socially-aware navigation planner, Trajectory 2014
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(2014) : A system for feature classification of emotions based on speech analysis; applications to human-robot interaction: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 795-800
DOI: https://doi.org/10.1109/ICRoM.2014.6991001 Abstract: A system for recognition of emotions based on speech analysis can have interesting applications in human robot interaction. Robot should make a proper mutual communication between sound recognition and perception for creating a desired emotional interaction with humans. Advanced research in this field will be based on sound analysis and recognition of emotions in spontaneous dialog. In this paper, we report the results obtained from an exploratory study on a methodology to automatically recognize and classify basic emotional states. The study attempted to investigate the appropriateness of using acoustic and phonetic properties of emotive speech with the minimal use of signal processing algorithms. The efficiency of the methodology was evaluated by experimental tests on adult European speakers. The speakers had to repeat six simple sentences in English language in order to emphasize features of the pitch (peak, value and range), the intensity of the speech, the formants and the speech rate. The proposed methodology using the freeware program (PRAAT) and consists of generating and analyzing a graph of pitch, formant and intensity of speech signals for classify basic emotion. Eventually, the proposed model provided successful recognition of the basic emotion in most of the cases.
Keywords: acoustic properties, Acoustics, adult European speakers, Angemessen(heit) (von Technik), emotion feature classification, emotion recognition, emotional interaction, emotional state classification, emotional state recognition, emotive speech, English language, Feature extraction, formant, formants, graph analysis, graph generation, graph theory, human-robot interaction, ieee xplore, mutual communication, phonetic properties, pitch, pitch features, PRAAT freeware program, public domain software, Shape, signal classification, signal processing algorithms, sound analysis, sound perception, sound recognition, speech, speech analysis, speech intensity, speech rate, speech recognition, speech signals, spontaneous dialog -
(2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190
DOI: https://doi.org/10.1109/ICRoM.2014.6990898 Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.
Keywords: Accuracy, Angemessen(heit) (von Technik), body gesture, eight-state HMM, Feature extraction, Gesture recognition, head pose, Hidden Markov model, Hidden Markov models, human social behavior, human-human interaction scenes, humanlike robot, humanoid social robot, human-robot interaction, ieee xplore, image colour analysis, image sensors, Joints, Kinect sensor, Microsoft Kinect SDK, pose estimation, RGB-D based social behavior interpretation system, RGB-D scene, Robot sensing systems, robot vision, social behavior interpretation system, social behavior recognition, Vectors, weighted average recognition rate 2013
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(2013) : Personal service: A robot that greets people individually based on observed behavior patterns: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Tokyo, Japan: IEEE, S. 129-130
DOI: https://doi.org/10.1109/HRI.2013.6483535 Abstract: We are developing an interactive service robot which provides personal greetings to customers, using a machine-learning approach based on observations of a customer’s appearance or behavior from on-board or environmental sensors. For each visit, several features are recorded, such as “time of day” or “number of people in group.” A set of classifiers trained by human coders compare the current features with the person’s individual history, to determine an appropriate feature for a robot to speak about. This system enables the robot to make context-appropriate comments such as “good morning, you’re here very early today.” We present the design of our system and an encouraging set of preliminary prediction results based on one month of data taken from real customers at a shopping mall.
Keywords: Accuracy, Angemessen(heit) (von Technik), context-appropriate comments, customer appearance, customer behavior, environmental sensors, Feature extraction, History, human coders, human-robot interaction, ieee xplore, interactive service robot, learning (artificial intelligence), long-term interaction, machine-learning approach, observed behavior patterns, personal greetings, personal service, Robot sensing systems, Sensors, service robot, shopping mall 2011
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(2011) : A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS: Proceedings of the Joint INDS’11 ISTET’11: Klagenfurt, Austria: IEEE, S. 1-5
DOI: https://doi.org/10.1109/INDS.2011.6024783 Abstract: This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.
Keywords: ADAS, Advanced driver assistance systems, Angemessen(heit) (von Technik), audio signal processing, audio streaming, audio streams, Bayes methods, Bayesian methods, Bayesian quadratic discriminate classifier, driver information systems, emotion feature extraction, emotion recognition, Feature extraction, ieee xplore, real-time emotion detection system, real-time emotion recognition concept, road accidents, speech, speech recognition, Support vector machines, traffic accidents, Vehicles, voice streams -
(2011): Multimodal integration processes in plan-based service robot control. In: Tsinghua Science and Technology 16 (1), S. 1-6. DOI: 10.1016/S1007-0214(11)70001-3
DOI: https://doi.org/10.1016/S1007-0214(11)70001-3 Abstract: Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment. We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER.
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