Name
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 quadrupedal robot. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6907476 | Robotics and Automation |
Keywords | Field | DocType |
legged locomotion,optimisation,StarlETH,agile gaits,automatic discovery,autonomous quadrupedal robot,controller parameterization,flying trot,legged robots,motor learning principles,motor skills,optimization approach,quadrupedal robots,scalable control architecture | Gait,Motor learning,Motor skill,Agile software development,Control engineering,Grand Challenges,Artificial intelligence,Engineering,Robot,Robotics,Scalability | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1050-4729 |
Citations | PageRank | References |
8 | 0.53 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Gehring | 1 | 180 | 13.79 |
| Stelian Coros | 2 | 862 | 56.47 |
| Marco Hutter | 3 | 460 | 58.00 |
| Michael Blösch | 4 | 427 | 31.24 |