A tripartite plan-based model of dialogue
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Task planning for human-robot interaction
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Who will be the customer?: a social robot that anticipates people's behavior from their trajectories
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
The oz of wizard: simulating the human for interaction research
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
How to approach humans?: strategies for social robots to initiate interaction
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Constraint task-based control in industrial settings
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Human-aware task planning: An application to mobile robots
ACM Transactions on Intelligent Systems and Technology (TIST)
Introducing animatronics to HCI: extending reality-based interaction
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: interaction techniques and environments - Volume Part II
How do people walk side-by-side?: using a computational model of human behavior for a social robot
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Timing multimodal turn-taking for human-robot cooperation
Proceedings of the 14th ACM international conference on Multimodal interaction
Levels of human and robot collaboration for automotive manufacturing
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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A crucial skill for fluent action meshing in human team activity is a learned and calculated selection of anticipatory actions. We believe that the same holds for robotic teammates, if they are to perform in a similarly fluent manner with their human counterparts.In this work, we propose an adaptive action selection mechanism for a robotic teammate, making anticipatory decisions based on the confidence of their validity and their relative risk. We predict an improvement in task efficiency and fluency compared to a purely reactive process.We then present results from a study involving untrained human subjects working with a simulated version of a robot using our system. We show a significant improvement in best-case task efficiency when compared to a group of users working with a reactive agent, as well as a significant difference in the perceived commitment of the robot to the team and its contribution to the team's uency and success. By way of explanation, we propose a number of fluency metrics that differ significantly between the two study groups.