Learning Compliant Locomotion on a Quadruped Robot

Over the last decades, compliant locomotion and manipulation have become a very active field of research, due to the versatility that robots with such capabilities would offer in many applications. With very few exceptions, generally, robotic applications and experiments take place in controlled environments. One of the reasons of this limited use is that in real world scenarios, robots need to interact with an unknown environment. In order to have a safe interaction with the environment while successfully performing some meaningful task, robots need to be compliant, such as humans and animals are. In this paper, a framework for optimizing stereotypical trotting gait of a quadruped robot by the use of variable impedance control is proposed. The framework uses the Policy Improvements with Path Integrals algorithm (PI) to optimize a parametrized gait generator and the robot’s impedance during locomotion. As result, it achieves an energetically efficient and robust trotting gait for different speeds while respecting the joints and actuators limits. The resulting controllers have been tested in a physics based simulator.