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.
2016 IEEE International Conference on Robotics and Automation (ICRA)