An Incremental Learning Framework for Human-like Redundancy Optimization of Anthropomorphic Manipulators

Recently, the human-like behavior on anthropomorphic robot manipulators are increasingly accomplished by the kinematic model estabilshing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced techniques in data science facilitate the imitation learning process in anthropomorphic robotics. However, the enormous data set causes the labeling and prediction burden. In this paper, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network (IN-DCNN) is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then to evolve its hierarchical representation of features. The incremental learning process is capable of fine-tuning the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). The anthropomorphic kinematic structure based redundant robots can be held by this approach. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data