Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Understanding Purposeful Human Motion
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Achieving fluency through perceptual-symbol practice in human-robot collaboration
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Ensemble: fluency and embodiment for robots acting with humans
Ensemble: fluency and embodiment for robots acting with humans
Sphinx-4: a flexible open source framework for speech recognition
Sphinx-4: a flexible open source framework for speech recognition
Cost-Based Anticipatory Action Selection for Human–Robot Fluency
IEEE Transactions on Robotics
EMA: A process model of appraisal dynamics
Cognitive Systems Research
Will i bother here?: a robot anticipating its influence on pedestrian walking comfort
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Useful and motivating robots: the influence of task structure on human-robot teamwork
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Timing in human-robot interaction
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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With the aim of attaining increased fluency and efficiency in human-robot teams, we have developed a cognitive architecture for robotic teammates based on the neuro-psychological principles of anticipation and perceptual simulation through top-down biasing. An instantiation of this architecture was implemented on a non-anthropomorphic robotic lamp, performing a repetitive human-robot collaborative task.In a human-subject study in which the robot works on a joint task with untrained subjects, we find our approach to be significantly more efficient and fluent than in a comparable system without anticipatory perceptual simulation. We also show the robot and the human to improve their relative contribution at a similar rate, possibly playing a part in the human's "like-me" perception of the robot.In self-report, we find significant differences between the two conditions in the sense of team fluency, the team's improvement over time, the robot's contribution to the efficiency and fluency, the robot's intelligence, and in the robot's adaptation to the task. We also find differences in verbal attitudes towards the robot: most notably, subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence, as well as credit for success, but we also find increased self-blame and self-deprecation in these subjects' responses.We believe that this work lays the foundation towards modeling and evaluating artificial practice for robots working in collaboration with humans.