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 2015
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(2015) : A model of empathy to shape trolley problem moral judgements: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII): Xian, China: IEEE, S. 112-118
DOI: https://doi.org/10.1109/ACII.2015.7344559 Abstract: Moral judgements are a complex phenomenon that have gained a renewed interest in the research community. Many have proposed explanations for moral judgements, including utilitarian accounts and the Principle of Double Effect. Some also advocate for the critical role of emotional processes like empathy. However, developing a computational model of moral judgements is rare perhaps due in part to the numerous influences on it. We present here a computational model of moral judgements based on moral expectation and the Principle of Double Effect. We then extend this model to provide a plausible explanation for the effect of empathy on these judgements. We evaluate these models using results from recent studies with human participants.
Keywords: Cognition, Complexity theory, computational model, Computational modeling, Decision Making, Decision theory, double effect principle, Empathy, empathy model, Ethics, ieee xplore, Moral & Ethik, moral expectation, Moral judgment, PSYCHOLOGY, shape trolley problem moral judgements, Trajectory, utility 2014
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(2014) : How to train your robot - teaching service robots to reproduce human social behavior: The 23rd IEEE International Symposium on Robot and Human Interactive Communication: Edinburgh, Scotland: IEEE, S. 961-968
DOI: https://doi.org/10.1109/ROMAN.2014.6926377 Abstract: Developing interactive behaviors for social robots presents a number of challenges. It is difficult to interpret the meaning of the details of people’s behavior, particularly non-verbal behavior like body positioning, but yet a social robot needs to be contingent to such subtle behaviors. It needs to generate utterances and non-verbal behavior with good timing and coordination. The rules for such behavior are often based on implicit knowledge and thus difficult for a designer to describe or program explicitly. We propose to teach such behaviors to a robot with a learning-by-demonstration approach, using recorded human-human interaction data to identify both the behaviors the robot should perform and the social cues it should respond to. In this study, we present a fully unsupervised approach that uses abstraction and clustering to identify behavior elements and joint interaction states, which are used in a variable-length Markov model predictor to generate socially-appropriate behavior commands for a robot. The proposed technique provides encouraging results despite high amounts of sensor noise, especially in speech recognition. We demonstrate our system with a robot in a shopping scenario.
Keywords: abstraction, Angemessen(heit) (von Technik), Cameras, clustering, human social behavior reproduction, human-human interaction data, human-robot interaction, ieee xplore, Joints, learning by example, learning-by-demonstration approach, Markov processes, pattern clustering, Robot sensing systems, robot training, service robot, service robot teaching, shopping scenario, socially-appropriate behavior command generation, speech, speech recognition, Trajectory, unsupervised approach, unsupervised learning, variable-length Markov model predictor 2010
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(2010) : Secondary action in robot motion: 19th International Symposium in Robot and Human Interactive Communication: Viareggio, Italy: IEEE, S. 310-315
DOI: https://doi.org/10.1109/ROMAN.2010.5598730 Abstract: Secondary action, a concept borrowed from character animation, improves the animation realism by augmenting natural, passive motion to primary action. We use dynamic simulation to induce three techniques of secondary motion for robot hardware, which exploit actuation passivity to overcome hardware constraints and change the dynamic perception of the robot and its motion characteristics. Results of secondary motion due to internal and external forces are presented including discussion on how to choose the appropriate technique for a particular application.
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(2010) : Smooth collision avoidance in human-robot coexisting environment: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems: Taipei, Taiwan: IEEE, S. 3887-3892
DOI: https://doi.org/10.1109/IROS.2010.5649673 Abstract: In order for service robots to safely coexist with humans, collision avoidance with humans is the most important issue. On the other hand, working efficiencies are also important and cannot be ignored. In this paper, we propose a method to estimate a pedestrian’s behavior. Based on the estimation, we realize smooth collision avoidances between a robot and a human. A robot detects pedestrians by using a laser range finder and tracks them by a Kalman filter. We apply the social force model to the observed trajectory for a determination whether the pedestrian intends to avoid a collision with the robot or not. The robot selects an appropriate behavior based on the estimation results. We conducted experiments that a robot and a person pass each other. Through the experiments, the usefulness of the proposed method was demonstrated.
Keywords: Angemessen(heit) (von Technik), Collision avoidance, Force, human-robot coexisting environment, human-robot interaction, ieee xplore, Kalman filter, Kalman filters, laser range finder, laser ranging, Leg, Mobile robots, pedestrian behavior, Robot kinematics, service robot, smooth collision avoidance, Trajectory
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