Learning Bionic Motions by Imitating Animals

Motion control algorithms for quadruped robots undergo rapid development in recent years. Interactive quadruped robots have demonstrated they may positively enhance the effect of psychotherapy in the treatment of patients with cognitive impairment, which requires them to have more interactive capabilities than traditional quadruped robots. In this study, we focus on enabling interactive quadruped robots to imitate real animal motions extracted from videos, by which the design of robotic motion controllers can be simplified and the bionic degree and the interactive capabilities of the robots can be enhanced. The motion capture data, however, cannot be directly utilized by the motion controllers since the robots and the real animals differ in their respective body geometries, motion dynamics and the numbers of DOF. To address these differences, we propose two strategies for imitating two different kind of motions. For ordinary motions (head scratching, waving, etc.), we first apply a scaling method to motion captured data and then use an inverse kinematic algorithm for imitation. Furthermore, to minimize the error of motion trajectories between the real animals and the robots, we then transform motion trajectories into a nonlinear optimization problem. For walking motions, we first analyze a classical SLIP model-based walking control algorithm for quadruped robots, and then apply the parameters extracted from motion captured data to the walking control algorithm. Experiments based on an interactive quadruped robot we developed demonstrate that our proposed strategies have great potential in improving the imitation capability of robots on the motions of real animals.