From first contact to close encounters: a developmentally deep perceptual system for a humanoid robot
Reconstructing force-dynamic models from video sequences
Artificial Intelligence
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous Learning of Object Appearances using Colour Contour Frames
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Fusion Algorithm for Locally Arranged Linear Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
What is an Appropriate Theory of Imitation for a Robot Learner?
LAB-RS '08 Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems
Real-time visual recognition of objects and scenes using P-channel matching
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
IEEE Transactions on Evolutionary Computation
Exploratory learning structures in artificial cognitive systems
Image and Vision Computing
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This paper presents an approach to problem solving through imitation. It introduces the Statistical and Temporal Percept Action Coupling (ST-PAC) System which statistically models the dependency between the perceptual state of the world and the resulting actions that this state should elicit. The ST-PAC system stores a sparse set of experiences provided by a teacher. These memories are stored to allow efficient recall and generalisation over novel systems states. Random exploration is also used as a fall-back ''brute-force'' mechanism should a recalled experience fail to solve a scenario. Statistical models are used to couple groups of percepts with similar actions and incremental learning used to incorporate new experiences into the system. The system is demonstrated within the problem domain of a children's shape sorter puzzle. The ST-PAC system provides an emergent architecture where competence is implicitly encoded within the system. In order to train and evaluate such emergent architectures, the concept of the Complexity Chain is proposed. The Complexity Chain allows efficient structured learning in a similar fashion to that used in biological system and can also be used as a method for evaluating a cognitive system's performance. Tests demonstrating the Complexity Chain in learning are shown in both simulated and live environments. Experimental results show that the proposed methods allowed for good generalisation and concept refinement from an initial set of sparse examples provided by a tutor.