Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Multiple model-based reinforcement learning
Neural Computation
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Variational Learning for Switching State-Space Models
Neural Computation
Integration of multi-level postural balancing on humanoid robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Mosaic for multiple-reward environments
Neural Computation
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In this study, we propose a novel extension of the MOSAIC architecture to control real humanoid robots. The MOSAIC architecture was originally proposed by neuroscientists to clarify the human ability of adaptive control. The modular architecture of the MOSAIC model can be useful for solving nonlinear and nonstationary control problems. Both humans and humanoid robots have nonlinear body dynamics and many degrees of freedom. In addition, they can carry objects, and this makes the dynamics nonstationary. Therefore, the MOSAIC architecture can be considered a promising candidate as a motor-control model of humans and a control framework for humanoid robots. However, the application of the MOSAIC model has been limited to simple simulated dynamics. Since each module of the MOSAIC has a forward model, we can adopt this model to construct a state estimator. By using the state estimators, the extended MOSAIC model can deal with large observation noise and partially observable systems. Thanks to these advantages, the proposed control framework can be applied to real systems such as humanoid robots.