One of the main challenges for modeling human-robot interactions (pHRI) is the high dimensionality and complexity of human motion. We present an integrated physical-learning modeling framework for pHRI with applications on the bikebot riding example. The modeling framework contains a machine learning-based model of high-dimensional limb motion coupled with a physical principle-based dynamic model for the human trunk and the interacted robot. A new axial linear embedding (ALE) algorithm is introduced to obtain the lower-dimensional latent dynamics for redundant human limb motion. The integrated physical-learning model is used to estimate the human motion through an extended Kalman filter design. No sensors are required and attached on human subject’s limb segments. Extensive bikebot riding experiments are conducted to validate the integrated human motion model. Comparison results with other machine learning-based models are also presented to demonstrate the superior performance of the proposed modeling framework.

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