A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A biologically inspired robotic model for learning by imitation
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Challenges in building robots that imitate people
Imitation in animals and artifacts
Wide-Range, Person- and Illumination-Insensitive Head Orientation Estimation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Accelerating reinforcement learning through imitation
Accelerating reinforcement learning through imitation
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
ICML '05 Proceedings of the 22nd international conference on Machine learning
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Emergence of Mirror Neurons in a Model of Gaze Following
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning to Attend -- From Bottom-Up to Top-Down
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Three-dimensional face pose detection and tracking using monocular videos: tool and application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Controlling gaze with an embodied interactive control architecture
Applied Intelligence
Efficacy of gesture for communication among humanoid robots by fuzzy inference method
International Journal of Computational Vision and Robotics
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An important component of language acquisition and cognitive learning is gaze imitation. Infants as young as one year of age can follow the gaze of an adult to determine the object the adult is focusing on. The ability to follow gaze is a precursor to shared attention, wherein two or more agents simultaneously focus their attention on a single object in the environment. Shared attention is a necessary skill for many complex, natural forms of learning, including learning based on imitation. This paper presents a probabilistic model of gaze imitation and shared attention that is inspired by Meltzoff and Moore's AIM model for imitation in infants. Our model combines a probabilistic algorithm for estimating gaze vectors with bottom-up saliency maps of visual scenes to produce maximum a posteriori (MAP) estimates of objects being looked at by an observed instructor. We test our model using a robotic system involving a pan-tilt camera head and show that combining saliency maps with gaze estimates leads to greater accuracy than using gaze alone. We additionally show that the system can learn instructor-specific probability distributions over objects, leading to increasing gaze accuracy over successive interactions with the instructor. Our results provide further support for probabilistic models of imitation and suggest new ways of implementing robotic systems that can interact with humans over an extended period of time.