Adaptive Behavior
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Multiple model-based reinforcement learning
Neural Computation
Inter-module credit assignment in modular reinforcement learning
Neural Networks
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Variational Learning for Switching State-Space Models
Neural Computation
Reinforcement Learning in Continuous Time and Space
Neural Computation
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Integration of multi-level postural balancing on humanoid robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Survey Research on gain scheduling
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
In this study, we propose an extension of the MOSAIC architecture to control real humanoid robots. MOSAIC was originally proposed by neuroscientists to understand the human ability of adaptive control. The modular architecture of the MOSAIC model can be useful for solving nonlinear and non-stationary control problems. Both humans and humanoid robots have nonlinear body dynamics and many degrees of freedom. Since they can interact with environments (e.g., carrying objects), control strategies need to deal with non-stationary dynamics. Therefore, MOSAIC has strong potential as a human motor-control model and a control framework for humanoid robots. Yet application of the MOSAIC model has been limited to simple simulated dynamics since it is susceptive to observation noise and also cannot be applied to partially observable systems. Our approach introduces state estimators into MOSAIC architecture to cope with real environments. By using an extended MOSAIC model, we are able to successfully generate squatting and object-carrying behaviors on a real humanoid robot.