Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Intelligence Review - Special issue on lazy learning
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Lagrangian support vector machines
The Journal of Machine Learning Research
Reinforcement learning with via-point representation
Neural Networks
Biological Cybernetics - Special Issue: Dynamic Principles
Planning Algorithms
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate 'modes' and hierarchical structure of the controller is constructed based on the modes. This enables efficient planning and control in low-dimensional planning space, though the dimension of the total state space is in general very high. Three types of object manipulation tasks are tested as applications, where an object is found and used as a tool (or as a part of the body) to extend the ability of the robot. The proposed framework is expected to be a basic learning model to account for body schema acquisition including tool affordances.