Adaptive control of mechanical manipulators
Adaptive control of mechanical manipulators
Technical Note: \cal Q-Learning
Machine Learning
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Coordination motion-tasks using actual robot dynamics
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Coordination motion-tasks using actual robot dynamics
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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International Journal of Robotics Research
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This paper describes a method for structuring a robot motor learning task. By designing a suitably parameterized policy, we show that a simple search algorithm, along with biologically motivated constraints, offers an effective means for motor skill acquisition. The framework makes use of the robot counterparts to several elements found in human motor learning: imitation, equilibrium-point control, motor programs, and synergies. We demonstrate that through learning, coordinated behavior emerges from initial, crude knowledge about a difficult robot weightlifting task.