Artificial Intelligence
Optimal composition of real-time systems
Artificial Intelligence
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Modeling embodied visual behaviors
ACM Transactions on Applied Perception (TAP)
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Coordinating with the Future: The Anticipatory Nature of Representation
Minds and Machines
Peripheral-foveal vision for real-time object recognition and tracking in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Goal recognition over POMDPs: inferring the intention of a POMDP agent
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Motor simulation via coupled internal models using sequential Monte Carlo
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A Computational Learning Theory of Active Object Recognition Under Uncertainty
International Journal of Computer Vision
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Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision. We propose a probabilistic generative framework based on information gain maximisation and a mixture of Kalman Filters that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration. Interestingly, the sensors control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness. Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems.