Alle Publikationen
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2019
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(2019): Human Understanding of Robot Motion: The Role of Velocity and Orientation. In: International Journal of Social Robotics 11 (1), S. 75-88. DOI: 10.1007/s12369-018-0493-4
DOI: https://doi.org/10.1007/s12369-018-0493-4 Abstract: A general problem in human–robot interaction is how to test the quality of single robot behavior, in order to develop robust and human-acceptable skills. The most typical approach are user tests with subjective measures (questionnaires). We propose a new experimental paradigm that combines subjective measures with an objective behavioral measure, namely viewing times of images viewed as self-paced slide show. We applied this paradigm to human-aware robot navigation. With three experiments, we studied the influence of two aspects of robot motion: velocity profiles and the robot’s orientation. A decreasing velocity profile influenced the predictability of the observed motion, and robot orientations diverting from the robot’s motion vector caused reduced perceived autonomy ratings. We conclude that the viewing time paradigm is a promising tool for studying human-aware robot behavior and that the design of human-aware robot navigation needs to consider both the velocity and the orientation of robots.
2018
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(2018) : Expression of intention by rotational head movements for teleoperated mobile robot: 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC): 2018 15th International Workshop on Advanced Motion Control (AMC): Tokyo: 3/9/2018 - 3/11/2018: [S.l.]: IEEE, S. 249-254
Abstract: We are studying a teleoperated mobile robot that provides useful information to a pedestrian. However, it is difficult for people to understand meanings of actions, motions or movements of many conventional robots. The purpose of this study is to improve pedestrian's impressions of a robot. Especially this paper describes people's understandability of robot behaviors when a robot turns around a corner or when a person and a robot pass each other in a corridor. Our robot shows its intention to make turn by rotating its head, as though a pedestrian shows a traveling direction by his/her gaze or face direction. The robot is teleoperated by an operator for safety in public spaces, and the direction of the robot head and the moving direction of the robot body are determined by an artificial potential field (APF) generated by a target position given by the operator, positions of obstacles and pedestrians. The APF for a pedestrian is generated based on her/his personal space of a person. Thus, the robot can express the intention of its action by rotating the head to look where it is going, when the robot changes its direction around pedestrians. The intention expression can be natural and understandable for them by the rotational movement of the head before the robot turns its body actually. Impression evaluation experiments with questionnaires were conducted under the two kinds of situations to reveal the validity and effectiveness of the intention expression by the robot's head rotation. Significant differences related to understandability and some impression words were observed between with and without rotating the head.
2015
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(2015) : Social context perception for mobile robots In: Burgard, Wolfram: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Sept. 28, 2015 - Oct. 2, 2015, Hamburg, Germany: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Hamburg, Germany: 9/28/2015 - 10/2/2015. IEEE/RSJ International Conference on Intelligent Robots and Systems; Iros: Piscataway, NJ: IEEE, S. 3621-3627
Abstract: As robots enter human spaces, unique perception challenges are emerging. Sensing human activity, adapting to highly dynamic environments, and acting coherently and contingently is challenging when robots transition from structured environments to human-centric ones. We approach this problem by employing context-based perception, a biologically-inspired, low-cost approach to sensing that leverages noisy, global features. Across several months, our mobile robot collected real-world, multimodal data from multi-use locations; where the same space might be used for many different activities. We then ran a series of unimodal and multimodal classification experiments. We successfully classified several aspects of situational context from noisy data, and, to our knowledge are the first group to do so. This work represents an important step toward enabling robots that can readily leverage context to solve perceptual tasks.
2013
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(2013) : Feature-Based Prediction of Trajectories for Socially Compliant Navigation In: Roy, Nicholas; Newman, Paul; Srinivasa, Siddhartha (Hg.): Robotics: Science and systems VIII: Cambridge, Massachusetts: The MIT Press
DOI: https://doi.org/10.7551/mitpress/9816.003.0030 Abstract: Mobile robots that operate in a shared environmentwith humans need the ability to predict the movements ofpeople to better plan their navigation actions. In this paper, wepresent a novel approach to predict the movements of pedestrians.Our method reasons about entire trajectories that arise frominteractions between people in navigation tasks. It applies amaximum entropy learning method based on features that capturerelevant aspects of the trajectories to determine the probabilitydistribution that underlies human navigation behavior. Hence, ourapproach can be used by mobile robots to predict forthcominginteractions with pedestrians and thus react in a socially compliantway. In extensive experiments, we evaluate the capability andaccuracy of our approach and demonstrate that our algorithmoutperforms the popular social forces method, a state-of-the-artapproach. Furthermore, we show how our algorithm can be usedfor autonomous robot navigation using a real robot.
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