Adaptive Control for Evolutionary Robotics
And its effect on learning directed locomotion
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Abstract
This thesis is motivated by evolutionary robot systems where robot bodies and brains evolve simultaneously. In such a robot system, `birth' must be followed by `infant learning' by a learning method that works for various morphologies evolution may produce. Here we address the task of directed locomotion in modular robots with controllers based on Central Pattern Generators. We present a bio-inspired adaptive feedback mechanism that uses a forward model and an inverse model that can be learned on-the-fly. We compare two versions (a simple and a sophisticated one) of this concept to a traditional (open-loop) controller using Bayesian Optimization as a learning algorithm.
The experimental results show that the sophisticated version outperforms the simple one and the traditional controller. It leads to improvement in performance and more robust controllers that cope better with noise.