Alle Publikationen
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2014
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(2014): Evaluation of Proxemic Scaling Functions for Social Robotics. In: IEEE Transactions on Human-Machine Systems 44 (3), S. 374-385. DOI: 10.1109/THMS.2014.2304075
DOI: https://doi.org/10.1109/THMS.2014.2304075 Abstract: This paper introduces and empirically evaluates two scaling functions to alter a robot’s physical movements based on proximity to a human. Previous research has focused on individual aspects of proxemics, like the appropriate distance to maintain from a human, but has not explored autonomous methods to adapt robot behavior as proximity changes. This paper proposes that robots in a social role should modify their behavior using a continuous function mapped to proximity. The method developed calculates a gain value from proximity readings, which is used to shape the execution of active behaviors on the robot. In order to identify the effects of different mappings from proximity to gain value, two different scaling functions were implemented on an affective search and rescue robot. The findings from a 72 participant study, in a high-fidelity mock disaster site, are examined with attention given to a new measure to determine proxemic awareness. The results indicated that for attributes of intelligence, likability, proxemic awareness, and submissiveness, a logarithmic-based scaling function is preferred over a linear-based scaling function, and over no scaling function. In areas of participant comfort and participant stress, the results indicated both logarithmic and linear scaling functions were preferred to no scaling.
Keywords: Angemessen(heit) (von Technik), Atmospheric measurements, autonomous methods, disasters, emergency services, high-fidelity mock disaster site, Human–robot interaction (HRI), human–robot proxemics, ieee xplore, intelligence attributes, Interpolation, Joints, Lighting, linear-based scaling function, logarithmic-based scaling function, Particle measurements, proxemic awareness, proxemic scaling function evaluation, Proxemics, rescue robots, robot physical movements, Robot sensing systems, search, service robot, Social robotic, social robots, submissiveness attributes -
(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 -
(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 -
(2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190
DOI: https://doi.org/10.1109/ICRoM.2014.6990898 Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.
Keywords: Accuracy, Angemessen(heit) (von Technik), body gesture, eight-state HMM, Feature extraction, Gesture recognition, head pose, Hidden Markov model, Hidden Markov models, human social behavior, human-human interaction scenes, humanlike robot, humanoid social robot, human-robot interaction, ieee xplore, image colour analysis, image sensors, Joints, Kinect sensor, Microsoft Kinect SDK, pose estimation, RGB-D based social behavior interpretation system, RGB-D scene, Robot sensing systems, robot vision, social behavior interpretation system, social behavior recognition, Vectors, weighted average recognition rate 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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