Explainable Robotics applied to bipedal gait development

Nico Roos, Zhenglong Sun

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Explainability is becoming an important topic in artificial intelligence (AI). A well explainable system can increase the trust in the application of that system. The same holds for robotics where the walking gait controller can be some AI system. We will show that a simple and explainable controller that enables an energy efficient walking gait and can handle uneven terrains, can be developed by a well structured design method. The main part of the controller consist of three simple neural networks with 4, 6 and 8 neurons. So, although creating a stable and energy efficient walking gait is a complex problem, it can be generated without some deep neural network or some complex mathematical model.
Original languageEnglish
Title of host publicationBeNeLux Artificial Intellignce Conference (BNAIC)
Subtitle of host publicationProceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg
PublisherCEUR Workshop Proceedings
Number of pages15
Publication statusPublished - 2019


  • bipedal robot
  • gait controller
  • uneven terrains

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