Скачать или смотреть [ICRA2020] Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking

We propose in this paper Periodic Interaction Primitives – a probablistic framework that can be used to learn compact models of periodic behavior. We show that this model is particularly well-suited for learning data-driven,
customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in an experiment with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and control signals and gracefully degrade in performance in the presence
of noise or sensor fall outs.