  Human motion prediction is an important component to facilitate human robot
interaction. Robots need to accurately predict human's future movement in order
to safely plan its own motion trajectories and efficiently collaborate with
humans. Many recent approaches predict human's movement using deep learning
methods, such as recurrent neural networks. However, existing methods lack the
ability to adapt to time-varying human behaviors, and many of them do not
quantify uncertainties in the prediction. This paper proposes an approach that
uses a semi-adaptable neural network for human motion prediction, and provides
uncertainty bounds of the predictions in real time. In particular, a neural
network is trained offline to represent the human motion transition model, and
then recursive least square parameter adaptation algorithm (RLS-PAA) is adopted
for online parameter adaptation of the neural network and for uncertainty
estimation. Experiments on several human motion datasets verify that the
proposed method significantly outperforms the state-of-the-art approach in
terms of prediction accuracy and computation efficiency.
