Alle Publikationen
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2018
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(2018) : A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 282-287
DOI: https://doi.org/10.1109/ROMAN.2018.8525577 Abstract: Autonomous robots are envisioned to increasingly become part of our lives in the house, restaurants, hospitals and offices. Additionally, self-driving cars will be soon appearing in city streets and highways and they will have to interact with cars driven by humans as well as other self-driving cars. In these settings the robots not only need to efficiently perform their tasks but also be able to interact with humans in socially appropriate ways. To accomplish this, robots must be able to reason not only on how to perform their tasks, but also incorporate societal values, social norms and legal rules so they can gain human acceptability and trust. Moreover, interactions with these robots will be long term. Long-term human interaction with robots as well as robot combined reasoning about both tasks and social norms generate multiple modeling and computational challenges. In this paper, we address one of the most important of these challenges, namely what is an appropriate and scalable computational framework that enables simultaneous task and normative reasoning. In particular, we report on our work on a novel computational framework, Modular Normative Markov Decision Processes (MNMDP) that integrates reasoning for domain tasks and normative reasoning for long-term autonomy. The MNMDP framework applies normative reasoning considering only the norms that are activated in appropriate contexts, rather than considering the full set of norms, thus significantly reducing computational complexity. The model modularity is also advantageous for long-term human-robot interaction. We present computational experiments that show significant computational improvements as compared with a base Normative Markov Decision Process (MDP) framework that includes the full set of norms.
Keywords: Angemessen(heit) (von Technik), Autonomous automobiles, autonomous robots, Cognition, Computational complexity, Decision theory, human acceptability, human-robot interaction, ieee xplore, inference mechanisms, knowledge based systems, long-term human interaction, Markov processes, MNMDP framework, Mobile robots, modular normative Markov decision processes, norm aware reasoning, normative Markov decision process framework, Normative reasoning, path planning, Planning, robot combined reasoning, self-driving cars, Social Norms, social robots, societal values, Task Analysis, task planning 2014
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(2014) : Personalizing robot behavior for interruption in social human-robot interaction: 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts: Evanston, IL: IEEE, S. 44-49
DOI: https://doi.org/10.1109/ARSO.2014.7020978 Abstract: People engaging in an activity usually has individual tolerance to be interrupted [1], [2]. Humans subconsciously adapt their behaviors to draw other one’s attention and to get into a conversation based on their historical experiences, but robots often fail to be aware of humans’ feeling and thus interrupt their users repeatedly. To endow service robots with such socially acceptable ability, we propose an online human-aware interactive learning framework in this paper, under which the robot personalizes its behaviors according to both observed user’s attention and its conjecture about user’s awareness of itself. To this purpose, the correlation between the robot’s theory of awareness, user’s attention and robot behavior are explored through reinforcement learning techniques. The conducted experiment shows that the robot can personalize its interruption strategy, and the optimal policies converged for at least 26 episodes.
Keywords: Face, Hidden Markov models, human-robot interaction, ieee xplore, Interrupters, interruption strategy, Künstliche Intelligenz, learning (artificial intelligence), Markov processes, online human-aware interactive learning framework, reinforcement learning techniques, robot behavior personalization, Robot sensing systems, robot theory of awareness, service robot, social human-robot interaction, social sciences, user attention, user awareness -
(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
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