As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills robots, where demonstrations represented in Cartesian or joint spaces are utilized estimate task/skill models that can be subsequently generalized new situations. While positions suffices for many applications, the end-effector orientation is required others. Despite recent advances orient...

Trainees differ in their preferences for learning strategies, and these differences can moderate the effectiveness of training programs. Unfortunately, previous attempts to develop learning style preference measures have not yielded reliable measures. The authors conducted two studies as part of an effort to develop and provide an initial validation of a learning style orientation measure. Fact...

Convolutional neural networks have many hyperparameters such as the filter size, number of filters, and pooling size, which require manual tuning. Though deep stacked structures are able to create multi-scale and hierarchical representations, manually fixed filter sizes limit the scale of representations that can be learned in a single convolutional layer. This paper introduces a new adaptive f...