Alle Publikationen
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2019
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(2019): Proactive Robots With the Perception of Nonverbal Human Behavior: A Review. In: IEEE Access 7, S. 77308-77327. DOI: 10.1109/ACCESS.2019.2921986
DOI: https://doi.org/10.1109/ACCESS.2019.2921986 Abstract: Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore, human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot's behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of human-robot interaction, social robotics and psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Second, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements, are presented as an outcome of the review.
2018
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(2018) : IoT Applications in Smart Cities: A Perspective Into Social and Ethical Issues: 2018 IEEE International Conference on Smart Computing (SMARTCOMP): 2018 IEEE International Conference on Smart Computing (SMARTCOMP): Taormina, Sicily, Italy: IEEE, S. 387-392
DOI: https://doi.org/10.1109/SMARTCOMP.2018.00034 Abstract: The possibility of interconnecting any kind of device to the Internet is driving the adoption of the Internet of Things (IoT) paradigm also in a city environment. Many IoT applications are making the Smart City concept real, offering advanced services to citizens and city administrators. This evolution relies upon a seamless exchange of information among different systems. Exchanged data includes personal and/or critical information, thus requiring proper handling in order to avoid security and privacy issues. At the same time, recent developments in robotics are fostering the realization of autonomous agents (e.g., cars, buses, drones) that do not require human intervention. In the city of the future, many services will rely on autonomous agents. Also, autonomous agents (e.g., robots) will become more human alike, and will be able to show emotions. Therefore, they will replace humans in many city activities. In this paper, we first overview the expected evolution of IoT applications and, then, we briefly analyze the security, social, and ethical issues that are foreseen in a Smart City context.
2014
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(2014) : Towards Action Selection Under Uncertainty for a Socially Aware Robot Bartender: 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI): 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Bielefeld, Germany: Association for Computing Machinery, S. 158-159
Abstract: We describe how the state representation of a socially aware robot is being extended to handle uncertainty. It incorporates the full range of information provided by the input sensors, including the confidence of all hypotheses. We also show how the Interaction Manager is being updated to make use of the extended representation.
2013
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(2013) : Towards rich multimodal behavior in spoken dialogues with embodied agents: 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom): 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom): Budapest, Hungary: IEEE, S. 817-822
DOI: https://doi.org/10.1109/CogInfoCom.2013.6719212 Abstract: Spoken dialogue frameworks have traditionally been designed to handle a single stream of data - the speech signal. Research on human-human communication has been providing large evidence and quantifying the effects and the importance of a multitude of other multimodal nonverbal signals that people use in their communication, that shape and regulate their interaction. Driven by findings from multimodal human spoken interaction, and the advancements of capture devices and robotics and animation technologies, new possibilities are rising for the development of multimodal human-machine interaction that is more affective, social, and engaging. In such face-to-face interaction scenarios, dialogue systems can have a large set of signals at their disposal to infer context and enhance and regulate the interaction through the generation of verbal and nonverbal facial signals. This paper summarizes several design decision, and experiments that we have followed in attempts to build rich and fluent multimodal interactive systems using a newly developed hybrid robotic head called Furhat, and discuss issues and challenges that this effort is facing.
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(2013) : Towards adaptive robots based on interaction traces: A user study: 2013 16th International Conference on Advanced Robotics (ICAR): 2013 16th International Conference on Advanced Robotics (ICAR): Paris, France: IEEE, S. 1-6
DOI: https://doi.org/10.1109/ICAR.2013.6766484 Abstract: We focus on the problem of adaptivity of companion robots to their users. Until recently, propositions on the subject of intelligent service robots were mostly user independent. Our work is part of the FUI-RoboPopuli project, which concentrates on endowing entertainment companion robots with adaptive and social behaviour.We concentrate on the capacity of robots to learn how to adapt and personalize their behaviour according to their users. Markov Decision Processes (MDPs) are largely used for adaptive robots applications. Several approaches were proposed to decrease the sample complexity to learn the MDP model, including the reward function.We proposed in previous work, two learning algorithms to learn the MDP reward function through analysing interaction traces (i.e. the interaction history between the robot and their users) including users' feedback. The first algorithm is direct and certain. The second is able to detect the importance of certain information, regarding the users (profiles) and/or the environment, in the adaptation process. We present, in this paper, a user study in addition to simulated experiments. Those experiments prove that our proposed algorithms are able to learn, through interactions with real users, a reward function that leads to an adapted and personalised robot behaviour. We also show the ability of our algorithms to handle exceptions and ambiguities in users feedback during experiments with real users.
2012
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(2012) : Dumb robots, smart phones: A case study of music listening companionship: 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication: Paris, France: IEEE, S. 358-363
DOI: https://doi.org/10.1109/ROMAN.2012.6343779 Abstract: Combining high-performance, sensor-rich mobile devices with simple, low-cost robotic platforms could accelerate the adoption of personal robotics in real-world environments. We present a case study of this "dumb robot, smart phone" paradigm: a robotic speaker dock and music listening companion. The robot is designed to enhance a human¿s listening experience by providing social presence and embodied musical performance. In its initial application, it generates segmentspecific, beat-synchronized gestures based on the song's genre, and maintains eye-contact with the user. All of the robot's computation, sensing, and high-level motion control is performed on a smartphone, with the rest of the robot¿s parts handling mechanics and actuator bridging.
2011
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(2011): Integration of Speech and Action in Humanoid Robots: iCub Simulation Experiments. In: IEEE Transactions on Autonomous Mental Development 3 (1), S. 17-29. DOI: 10.1109/TAMD.2010.2100390
DOI: https://doi.org/10.1109/TAMD.2010.2100390 Abstract: Building intelligent systems with human level competence is the ultimate grand challenge for science and technology in general, and especially for cognitive developmental robotics. This paper proposes a new approach to the design of cognitive skills in a robot able to interact with, and communicate about, the surrounding physical world and manipulate objects in an adaptive manner. The work is based on robotic simulation experiments showing that a humanoid robot (iCub platform) is able to acquire behavioral, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to understand basic instructions, and to adapt its abilities to changes in internal and environmental conditions.
2010
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(2010) : People tracking with human motion predictions from social forces: 2010 IEEE International Conference on Robotics and Automation: 2010 IEEE International Conference on Robotics and Automation: Anchorage, Alaska, USA: IEEE, S. 464-469
DOI: https://doi.org/10.1109/ROBOT.2010.5509779 Abstract: For many tasks in populated environments, robots need to keep track of current and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity or acceleration. But even over a short period, human behavior is more complex and influenced by factors such as the intended goal, other people, objects in the environment, and social rules. This motivates the use of more sophisticated motion models for people tracking especially since humans frequently undergo lengthy occlusion events. In this paper, we consider computational models developed in the cognitive and social science communities that describe individual and collective pedestrian dynamics for tasks such as crowd behavior analysis. In particular, we integrate a model based on a social force concept into a multi-hypothesis target tracker. We show how the refined motion predictions translate into more informed probability distributions over hypotheses and finally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range finder, the social force model leads to more accurate tracking with up to two times fewer data association errors.
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