Learning new gaits for compliant robots is a challenging multi-dimensional optimization task. Furthermore, to ensure optimal performance, the optimization process must be repeated for every variation in the environment, e.g. for every change in inclination of the terrain. This is unfortunately not possible using current approaches, since the time required for the optimization is simply too high. Hence, a sub-optimal gait is often used. The goal in this manuscript is to reduce the learning time of a particle swarm algorithm, such that the robot’s gaits can be optimized over a wide variety of terrains. To facilitate this, we use transfer learning by sharing knowledge about gaits between the different environments. Our ﬁndings indicate that using transfer learning, new robust gaites can be discovered faster compared to traditional methods that learn a gait for each environment independently.
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