Journal
CONNECTION SCIENCE
Volume 23, Issue 4, Pages 239-283Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/09540091.2011.625077
Keywords
motor learning; internal models; neural networks; kinematics; robotics
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available