Impedance-based Gaussian Processes for predicting human behavior during physical interaction

For seamless physical human-robot interaction (pHRI), estimating human intention is essential. Most system identification approaches to pHRI model the human as a black box without prior assumptions about the underlying behavioral structure. However, integrating a priori knowledge about behavioral characteristics of the human provides superior prediction performance. In this work we present a novel method for human behavior prediction during physical interaction that incorporates an empirically supported human motor control model. The arm dynamics of the human are modeled as a mechanical impedance that follows a latent desired trajectory. We adopt a Bayesian perspective setting Gaussian Process (GP) priors on impedance parameters and the desired trajectory, which allows regression about human behavior from observed trajectories and interaction forces. The proposed impedance-based GP model is validated in simulation and in an experiment with human participants to demonstrate its prediction performance and generalization capability.