modeling and learning walking gaits of biped robots
Author(s): Matthias Hebbel, Ralf Kosse and Walter Nistico
Venue: IEEE-RAS International Conference of Humanoid Robots
Year Published: 2006
Keywords: locomotion, legged robots, genetic algorithms, evolution
Expert Opinion: This paper describes the open loop modelling of a robot gait which mimics the human walking style. The authors develop a parameterized model for the leg and arm motions. They then compare various machine learning methods for finding the best parameters, i.e., the ones that provide the best walk. The paper is very interesting as: - it is one of the pioneer works on robot gait learning - it rises many issues related to practical application of machine learning methods on real hardware - it gives many insights (again, mainly practical) on how to develop a robot learning framework. While the scientific contribution may be limited, the paper has a great importance for its presentation of practical issues. For this reason, I recommend its reading to young students interested in studying this topic for the first time.