Alle Publikationen
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2018
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(2018) : Adapting Robot Behavior using Regulatory Focus Theory, User Physiological State and Task-Performance Information: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 644-651
DOI: https://doi.org/10.1109/ROMAN.2018.8525648 Abstract: Social robots are expected to be part of everyday life of people. This will generate interactions between humans and robots that may have positive or negative effects on the users. In order to minimize the negative effects and increase robot persuasiveness, robots should behave in an appropriate manner by adapting to their users. How to achieve this adaptation remains a challenge. We propose the usage of the Regulatory Focus Theory, user physiological state, and game-performance information in order to detect user stress and adapt the behavior of the robot. We present a longitudinal experiment conducted with 35 participants in a game-like scenario. The robot was trained for adapting to the regulatory focus of the users and decreasing their stress while they were playing the game. For this reason, we trained the robot with 12 participants with Chronic Promotion State and with 12 participants with Chronic Prevention State. We used a Q-Learning algorithm based on the Regulatory Focus of the participants, user stress, and task performance. The model obtained was tested with 2 groups (6 and 5 participants, respectively) according to their Chronic Regulatory Focus. Results show that our system was able to generate a robot behavior capable of increasing robot persuasiveness and reducing user stress, which is of great importance for social robots.
Keywords: Adaptive systems, Angemessen(heit) (von Technik), chronic promotion state, chronic regulatory focus, game-like scenario, game-performance information, Games, human-robot interaction, ieee xplore, learning (artificial intelligence), physiology, regulatory focus theory, robot behavior, robot persuasiveness, Robot sensing systems, social robots, Stress, Task Analysis, Task Performance, task-performance information, user physiological state 2017
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(2017) : Designing technology for older adults: Augmenting usefulness and usability via cognitive support In: Kwon, Sunkyo: Gerontechnology: Research, practice, and principles in the field of technology and aging: New York, NY: Springer Publishing Company, S. 389-416
Abstract: To fully explore why age discrepancies in technology use have been observed, we describe how older adults conceptualize usefulness and how usability can be empirically measured. Because cognitive changes that co-occur with age can influence technology-related task performance, which in turn may either augment or reduce perceived usefulness and usability, we also discuss the importance of designing technology to provide cognitive and environmental support. Such supportive design is key in developing technology that older adults want to use, which furthers understanding of how age-related cognitive changes should be considered during the technology design process. Furthermore, this approach identifies research questions that should benefit from further empirical investigation. We focus on what is known about three predictor variables (i.e., usefulness, usability, and cognition) that deserve special consideration when designing technology for older adults’ use. To accomplish this goal, three approaches with examples are described in this chapter. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
2014
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(2014): Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction. In: IEEE Transactions on Haptics 7 (3), S. 367-380. DOI: 10.1109/TOH.2013.54
DOI: https://doi.org/10.1109/TOH.2013.54 Abstract: Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot Learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective.
Keywords: Algorithms, Analysis, Bedienung & Handhabung, Biomechanical Phenomena, compliance control, compliance variations, compliant control, compliant manipulation, Computer Simulation, education, Force, haptic feedback, haptic interfaces, human-robot interaction, Humans, Impedance, Joints, Kinesthesis, kinesthetic human-robot interaction, KUKA LWR robot manipulators, Learning, manipulator kinematics, Physical Human-Robot Interaction, position control, RLfD, Robot kinematics, robot learning from demonstration, Robot sensing systems, Robotics, stiff position controller, tactile human-robot interaction, tactile interfaces, task kinematics, task model, Task Performance, Touch
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