Practice Makes Perfect: An Optimization Based Approach to Controlling Agile Motions for a Quadruped Robot
This article approaches the problem of controlling quadrupedal running and jumping motions with a parameterized, model based, state feedback controller. Inspired by the motor learning principles observed in nature, our method automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn through practice process is performed in simulation to best exploit computational resources and to prevent the robot from damaging itself. To ensure that the simulation results match the behavior of the hardware platform, we introduce and validate an accurate model of the compliant actuation system. The proposed method is experimentally verified on the torque controllable quadruped robot StarlETH by executing squat jumps and dynamic gaits, such as a running trot, pronk, and a bounding gait.
IEEE Robotics Automation Magazine