Robots that have been optimized in simulation often underperform in the real world in comparison to their simulated counterparts. This difference in performance is often called a reality-gap. In this paper, we use two methods, genetic algorithm and topology optimization, to optimize a quadruped robot. We look at the original and optimized robots’ performance in simulation and reality and compare the results. Both methods show improvement in the robot’s efficiency, however the topology optimization behaves in a more predictable manner and shows similar results in simulation and in real laboratory testing. Modifying robot morphology with a genetic algorithm, although less predictable, has a potential for more improvement in efficiency.
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