Evolutionary Robotics Applied to Hexapod Locomotion: a Comparative Study of Simulation Techniques


The Evolutionary Robotics (ER) process has been applied extensively to developing control programs to achieve locomotion in legged robots, as an automated alternative to the arduous task of manually creating control programs for such robots. The evolution of such controllers is typically performed in simulation by making use of a physics engine-based robotic simulator. Making use of such physics-based simulators does, however, have certain challenges associated with it, such as these simulators’ computational inefficiency, potential issues with lack of accuracy and the human effort required to construct such simulators. The current study therefore proposed and investigated an alternative method of simulation for a hexapod (six-legged) robot in the ER process, and directly compared this newly-proposed simulation method to traditional physics-based simulation. This alternative robotic simulator was built based solely on experimental data acquired directly from observing the behaviour of the robot. This data was used to construct a simulator for the robot based on Artificial Neural Networks (ANNs). To compare this novel simulation method to traditional physics simulation, the ANN-based simulators were used to evolve simple open-loop locomotion controllers for the robot in simulation. The real-world performance of these controllers was compared to that of controllers evolved in a more traditional physics-based simulator. The obtained results indicated that the use of ANN-based simulators produced controllers which could successfully perform the required locomotion task on the real-world robot. In addition, the controllers evolved using the ANN-based simulators allowed the real-world robot to move further than those evolved in the physics-based simulator and the ANN-based simulators were vastly more computationally efficient than the physics-based simulator. This study thus decisively indicated that ANN-based simulators offer a superior alternative to widely-used physics simulators in ER for the locomotion task considered.

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Correspondence to Christiaan J. Pretorius.

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The financial assistance of the National Research Foundation (NRF) towards this research is hereby gratefully acknowledged (UID number: 79570). Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.

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Pretorius, C.J., du Plessis, M.C. & Gonsalves, J.W. Evolutionary Robotics Applied to Hexapod Locomotion: a Comparative Study of Simulation Techniques. J Intell Robot Syst 96, 363–385 (2019). https://doi.org/10.1007/s10846-019-00989-0

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  • Hexapod
  • Deep Learning
  • Evolutionary Robotics
  • Artificial Neural Networks
  • Locomotion
  • System identification