Behavior recognition with ground reaction force estimation and its application to imitation learning

In this paper, we propose an imitation learning framework to generate multiple behaviors with balance control by recognizing human behaviors while estimating the ground reaction force. In our proposed method, a part of captured human motion data is recognized as one particular behavior that is represented by a linear dynamical model. Therefore, our method has small dependence on a classification criteria defined by an experimenter. Based on the behavior recognition method with the ground reaction force estimation and by combining the different recognized behaviors, it is possible to generate many different motion sequences while taking balance into account. First, we approximate a human motion pattern by using linear dynamical models. Then, we can recognize and generate different behavior sequences by switching linear dynamical models. We apply the proposed method to a four-link simulated robot model. Two different squat motions are recognized from motion capture data and the four-link robot generated four different combined squat behaviors from two different squat motions. To show generalization performance, we apply our imitation learning framework to the four-link robot models that have different weights.