A Motion Generation Strategy of Robotic Rat Using Imitation Learning for Behavioral Interaction

In this letter, we propose a motion generation strategy using imitation learning (IL) for biomimetic robots to interact with animals in a more natural manner. We previously developed a bioinspired spine mechanism allowing the robot to mimic rat motion closely. Thanks to the spine morphology, we established the corresponding relationship of motion vector (spatial location and positions of key movement joints) between the robot and rats. We denote the motion vectors of two agents (demo robot and policy robot) as the interaction motion features (IMFs), where the motion vector of demo robot is directly copied from the demo rat and that of policy robot is generated using IL. First, we trained multilayer perceptrons by inputting the IMFs from the rat-rat interaction to generate the predicted IMFs for robot-robot interaction. Secondly, robot-robot IMFs are used to train a policy net, whose cost is the mean squared errors between the predicated and current features. Finally, we evaluated the proposed method in a simulation environment for the interaction between demo robot and policy robot. The results show effective interaction duration between robots has an increment of 16% than that between rats. Moreover, it also has a better concentration span than that of rat-rat interaction.