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2016
Ehrlich, S.; Cheng, G. (2016) : A neuro-based method for detecting context-dependent erroneous robot action: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids): Cancun, Mexico: IEEE, S. 477-482

DOI: https://doi.org/10.1109/HUMANOIDS.2016.7803318

Abstract: Validating appropriateness and naturalness of human-robot interaction (HRI) is commonly performed by taking subjective measures from human interaction partners, e.g. questionnaire ratings. Although these measures can be of high value for robot designers, they are very sensitive and can be inaccurate and/or biased. In this paper we propose and validate a neuro-based method for objectively validating robot behavior in HRI. We propose to detect from the electronencephalo-gram (EEG) of a human interaction partner, the perception of inappropriate / unexpected / erroneous robot behavior. To validate this method, we conducted an EEG experiment with a simplified HRI protocol in which a humanoid robot displayed context-dependent erroneous behavior from time to time. The EEG data taken from 13 participants revealed biologically plausible error-related potentials (ErrP) whose spatio-temporal distributions match well with related neuroscientific research. We further demonstrate that perceived erroneous robot action can reliably be modeled and detected from human EEG signals with classification accuracies on avg. 69.7±9.1%. These findings confirm principal feasibility of the proposed method and suggest that EEG-based ErrP detection can be used for quantitative evaluation and thus improvement of robot behavior.

Keywords: Angemessen(heit) (von Technik), Computers, context-dependent erroneous behavior, context-dependent erroneous robot action, EEG, Electroencephalography, electronencephalogram, error-related potentials, ErrP, HRI protocol, human interaction partners, Humanoid Robots, human-robot interaction, ieee xplore, Magnetic heads, neuro-based method, neurocontrollers, neuroscientific research, Protocols, robot behavior, robot designers, Robot sensing systems, spatio-temporal distributions
Palinko, O.; Rea, F.; Sandini, G.; Sciutti, A. (2016) : Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Daejeon, South Korea: IEEE Robotics & Automation Society, S. 5048-5054

DOI: https://doi.org/10.1109/IROS.2016.7759741

Abstract: Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naïve subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.

Keywords: Angemessen(heit) (von Technik), Cameras, communication cues, EYE GAZE, eye tracking system, eye-based gaze estimation, gaze tracking, Head, head orientation, head tracking, head-based gaze estimation, Human robot interaction, Humanoid Robots, human-robot collaboration, human-robot interaction, iCub robot, ieee xplore, Magnetic heads, performance improvements, pose estimation, quantitative measures, robot human gaze reading, Robot kinematics, robot perception, robot proxy, robot vision, Visualization
2014
Avrunin, E.; Simmons, R. (2014) : Socially-appropriate approach paths using human data: The 23rd IEEE International Symposium on Robot and Human Interactive Communication: Edinburgh, Scotland: IEEE, S. 1037-1042

DOI: https://doi.org/10.1109/ROMAN.2014.6926389

Abstract: For service robots operating in indoor environments, the crucial task of navigation is often complicated by the presence of people. Simply treating humans in the environment as additional (often moving) obstacles can violate the complex set of social rules by which people navigate around each other. In contrast, emulating human behavior and navigating in a socially appropriate manner could positively affect people’s comfort with a robot’s presence and motion. We present a method of generating social paths for a robot to approach a person based on a small amount of human data. We also conducted a study in which a robot approached participants using both these social paths and straight-line, nonsocial paths. We found that both approaches were rated comparably when the robot approached from the participant’s front or side, but the social approach was significantly preferred when the robot came from behind the participant.

Keywords: Angemessen(heit) (von Technik), Collision avoidance, Data models, Distance measurement, Human behavior, ieee xplore, Legged locomotion, Magnetic heads, motion control, moving obstacles, Navigation, navigation task, person approach, robot motion, robot presence, service robot, social path, social path generation, social rules, socially-appropriate approach path, straight-line nonsocial path, Torso
2007
Jaeckel, P.; Campbell, N.; Melhuish, C. (2007) : Towards Realistic Facial Behaviour in Humanoids - Mapping from Video Footage to a Robot Head: 2007 IEEE 10th International Conference on Rehabilitation Robotics: Piscataway NJ: IEEE, S. 833-840

DOI: https://doi.org/10.1109/ICORR.2007.4428521

Abstract: Rehabilitation robotics and physical therapy could greatly benefit from engaging and motivating, robotic caregivers which respond in accordance to patients’ emotional and social cues. Recent studies indicate that human-machine interactions are more believable and memorable when a physical entity is present, provided that the machine behaves in a realistic manner. It is desirable to adopt face-to-face communication because it is the most natural and efficient way of exchanging information and does not require users to alter their habits. Towards this end, we describe a process for animating a robot head, based on video input of a human head. We map from the 2D coordinates of feature points into the robot’s servo space using Partial Least Squares (PLS). Learning is done using a small set of keyframes manually created by an animator. The method is efficient, robust to tracking errors and independent of the scale of the face being tracked.

Keywords: Animation, Computer animation, Facial animation, human-machine interactions, Humanoid Robots, Humanoids, Humans, ieee xplore, Künstliche Intelligenz, Learning, learning (artificial intelligence), Magnetic heads, Man machine systems, medical robotics, Medical treatment, Orbital robotics, partial least squares, patient rehabilitation, physical therapy, realistic facial behaviour, Rehabilitation robotics, robot head, Robot kinematics, robot servo space, servomechanisms, video footage

2016

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2007

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