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摘要
En 中文
This paper focuses on adaptive motor control in the kinematic domain. Several motor-learning strategies from the literature are adopted to kinematic problems: 'feedback-error learning', 'distal supervised learning', and 'direct inverse modelling' (DIM). One of these learning strategies, DIM, is significantly enhanced by combining it with abstract recurrent neural networks. Moreover, a newly developed learning strategy ('learning by averaging') is presented in detail. The performance of these learning strategies is compared with different learning tasks on two simulated robot setups (a robot-camera-head and a planar arm). The results indicate a general superiority of DIM if combined with abstract recurrent neural networks. Learning by averaging shows consistent success if the motor task is constrained by special requirements.
Keyword:
motor learning
internal models
neural networks
kinematics
robotics
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3.4
论文数:
852
被引数:
1.5K
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