Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking | IEEE Conference Publication | IEEE Xplore

Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking


Abstract:

We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends e...Show More

Abstract:

We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. 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 experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21° in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
Date of Conference: 31 May 2020 - 31 August 2020
Date Added to IEEE Xplore: 15 September 2020
ISBN Information:

ISSN Information:

Conference Location: Paris, France

Contact IEEE to Subscribe

References

References is not available for this document.