Forward models for physiological motor control
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Speech motor control: acoustic goals, saturation effects, auditory feedback and internal models
Speech Communication - Special issue on speech production: models and data
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
2006 Special issue: Mirror neurons and imitation: A computationally guided review
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
2006 Special issue: Goals and means in action observation: A computational approach
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning forward models for robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Visual learning by imitation with motor representations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards active event recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We describe a generative Bayesian model for action understanding in which inverse-forward internal model pairs are considered 'hypotheses' of plausible action goals that are explored in parallel via an approximate inference mechanism based on sequential Monte Carlo methods. The reenactment of internal model pairs can be considered a form of motor simulation, which supports both perceptual prediction and action understanding at the goal level. However, this procedure is generally considered to be computationally inefficient. We present a model that dynamically reallocates computational resources to more accurate internal models depending on both the available prior information and the prediction error of the inverse-forward models, and which leads to successful action recognition. We present experimental results that test the robustness and efficiency of our model in real-world scenarios.