Towards Automatic Discovery of Agile Gaits for Quadrupedal Robots

 

Developing control methods that allow legged robots to move with skill and agility remains one of the grand challenges in robotics.

May 31, 2014
IEEE International Conference on Robotics and Automation (ICRA) 2014

 

Authors

Christian Gehring (Disney Research/ETH Joint PhD)

Stelian Coros (Disney Research)

Marco Hutter (ETH Zurich)

Michael Bloesch (ETH Zurich)

Peter Fankhauser (ETH Zurich)

Markus Hoepflinger (ETH Zurich)

Roland Siegwart (ETH Zurich)

Towards Automatic Discovery of Agile Gaits for Quadrupedal Robots

Abstract

Developing control methods that allow legged robots to move with skill and agility remains one of the grand challenges in robotics. In order to achieve this ambitious goal, legged robots must possess a wide repertoire of motor skills. A scalable control architecture that can represent a variety of gaits in a unified manner is therefore desirable. Inspired by the motor learning principles observed in nature, we use an optimization approach to automatically discover and fine-tune parameters for agile gaits. The success of our approach is due to the controller parameterization we employ, which is compact yet flexible, therefore lending itself well to learning through repetition. We use our method to implement a flying trot, a bound and a pronking gait for StarlETH, a fully autonomous, dog-sized quadrupedal robot.

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