Experiences with an interactive museum tour-guide robot
Artificial Intelligence - Special issue on applications of artificial intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Proceedings of the 9th international conference on Multimodal interfaces
Precision timing in human-robot interaction: coordination of head movement and utterance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Providing route directions: design of robot's utterance, gesture, and timing
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
An affective guide robot in a shopping mall
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Conversational gaze mechanisms for humanlike robots
ACM Transactions on Interactive Intelligent Systems (TiiS)
Robot behavior toolkit: generating effective social behaviors for robots
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
A hierarchical Bayesian framework for multimodal active perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Modeling speaker behavior: a comparison of two approaches
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
| Hi-index | 0.00 |
In order to communicate with their users in a natural and effective manner, humanlike robots must seamlessly integrate behaviors across multiple modalities, including speech, gaze, and gestures. While researchers and designers have successfully drawn on studies of human interactions to build models of humanlike behavior and to achieve such integration in robot behavior, the development of such models involves a laborious process of inspecting data to identify patterns within each modality or across modalities of behavior and to represent these patterns as "rules" or heuristics that can be used to control the behaviors of a robot, but provides little support for validation, extensibility, and learning. In this paper, we explore how a learning-based approach to modeling multimodal behaviors might address these limitations. We demonstrate the use of a dynamic Bayesian network (DBN) for modeling how humans coordinate speech, gaze, and gesture behaviors in narration and for achieving such coordination with robots. The evaluation of this approach in a human-robot interaction study shows that this learning-based approach is comparable to conventional modeling approaches in enabling effective robot behaviors while reducing the effort involved in identifying behavioral patterns and providing a probabilistic representation of the dynamics of human behavior. We discuss the implications of this approach for designing natural, effective multimodal robot behaviors.