Practice Makes Perfect: An Optimization-Based Approach to Controlling Agile Motions for a Quadruped Robot


This article approaches the matter 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 methodology automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn-through-apply method is performed in simulation to best exploit computational resources and to stop the robot from damaging itself. To make sure 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 methodology 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.

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