Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Three creatures named 'forward model'
Neural Networks
Imitation in animals and artifacts
Optimal robot arm control using the minimum variance model
Journal of Robotic Systems
Journal of Cognitive Neuroscience
Compound effects of top-down and bottom-up influences on visual attention during action recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Neural Model for the Visual Recognition of Goal-Directed Movements
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Efficient template-based path imitation by invariant feature mapping
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Teaching a humanoid robot to draw `Shapes'
Autonomous Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Internal simulations for behaviour selection and recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
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Recent computational approaches to action imitation have advocated the use of hierarchical representations in the perception and imitation of demonstrated actions. Hierarchical representations present several advantages, with the main one being their ability to process information at multiple levels of detail. However, the nature of the hierarchies in these approaches has remained relatively unsophisticated, and their relation with biological evidence has not been investigated in detail, in particular with respect to the timing of movements. Following recent neuroscience work on the modulation of the premotor mirror neuron activity during the observation of unpredictable grasping movements, we present here an implementation of our HAMMER architecture using the minimum variance model for implementing reaching and grasping movements that have biologically plausible trajectories. Subsequently, we evaluate the performance of our model in matching the temporal dynamics of the modulation of cortical excitability during the passive observation of normal and unpredictable movements of human demonstrators. ror neuron activity during observation of upredictable grasping movements. European Journal of Neuroscience, 20, 2193-2202], we present here an implementation of our HAMMER architecture [Demiris, Y., & Khadhouri, B. (in press). Hierarchical, attentive multiple models for execution and recognition. Robotics and Autonomous Systems] using the minimum variance model [Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394, 780-784; Simmons, G., & Demiris, Y. (2005). Optimal robot arm control using the minimum variance model. Journal of Robotic Systems, 22(11), 677-690] for implementing reaching and grasping movements that have biologically plausible trajectories. Subsequently, we evaluate the performance of our model in matching the temporal dynamics of the modulation of cortical excitability during the passive observation of normal and unpredictable movements of human demonstrators.