Robots execute a wide array of rhythmic behaviors like walking, running, dribbling, etc. Rhythmic movement primitives are parametric models that can be used to represent these behaviors. Previously, these models have been learned either by means of demonstrations or by means of reward signals. While executing a rhythmic behavior, a stabilizing controller is needed in most robots to prevent fail...

In this paper, we present our theoretical investigations of the technique of feedback error learning (FEL) from the viewpoint of adaptive control. We first discuss the relationship between FEL and nonlinear adaptive control with adaptive feedback linearization, and show that FEL can be interpreted as a form of nonlinear adaptive control. Second, we present a Lyapunov analysis suggesting that th...

When movements are perturbed in adaptation tasks, humans and other animals show incomplete compensation, tolerating small but sustained residual errors that persist despite repeated trials. State-space models explain this residual asymptotic error as interplay between learning from error and reversion to baseline, a form of forgetting. Previous work using zero-error-clamp trials has shown that ...

the present study-both qualitative and quantitative--explored fifty efl learners’ preferences for receiving error feedback on different grammatical units as well as their beliefs about teacher feedback strategies. the study also examined the effect of the students’ level of writing ability on their views about the importance of teacher feedback on different error types. data was gathered throug...

Humans monitor their behavior to optimize performance, which presumably relies on stable representations of correct responses. During second language (L2) learning, however, stable representations have yet to be formed while knowledge of the first language (L1) can interfere with learning, which in some cases results in persistent errors. In order to examine how correct L2 representations are s...

This paper presents an application of the feedback error learning technique for online control of an inverted pendulum which has uncertain friction nonlinearity. In order to build up online adaptive learning control, i) the preliminary offline training and the scaling factor for the neural network to escape from the local minimum, and ii) twostage learning scheme are introduced. After some lear...

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had...