Explainable robotics applied to bipedal walking gait development

Nico Roos; Zhenglong Sun

Explainable robotics applied to bipedal walking gait development

Nico Roos^*, Zhenglong Sun

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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Abstract

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 language	English
Title of host publication	Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg
Subtitle of host publication	31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019
Volume	2491
Publication status	Published - 1 Jan 2019
Event	31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning - Brussels, Belgium Duration: 6 Nov 2019 → 8 Nov 2019 Conference number: 31

Publication series

Series	CEUR Workshop Proceedings
ISSN	1613-0073

Conference

Conference	31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning
Abbreviated title	BNAIC/BENELEARN 2019
Country/Territory	Belgium
City	Brussels
Period	6/11/19 → 8/11/19

Access to Document

Full TextFinal published version, 1 MBLicence: Taverne

https://dke.maastrichtuniversity.nl/nico.roos/wp-content/uploads/2020/01/Roos19BNAIC.pdf

Cite this

Roos, N., & Sun, Z. (2019). Explainable robotics applied to bipedal walking gait development. In Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg: 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 (Vol. 2491). Article 153783 https://dke.maastrichtuniversity.nl/nico.roos/wp-content/uploads/2020/01/Roos19BNAIC.pdf

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title = "Explainable robotics applied to bipedal walking gait development",

abstract = "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.",

author = "Nico Roos and Zhenglong Sun",

note = "Publisher Copyright: {\textcopyright} 2019 for this paper by its authors.; 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 ; Conference date: 06-11-2019 Through 08-11-2019",

year = "2019",

month = jan,

day = "1",

language = "English",

volume = "2491",

series = "CEUR Workshop Proceedings",

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Roos, N & Sun, Z 2019, Explainable robotics applied to bipedal walking gait development. in Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg: 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019. vol. 2491, 153783, CEUR Workshop Proceedings, 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, Brussels, Belgium, 6/11/19. <https://dke.maastrichtuniversity.nl/nico.roos/wp-content/uploads/2020/01/Roos19BNAIC.pdf>

Explainable robotics applied to bipedal walking gait development. / Roos, Nico; Sun, Zhenglong.
Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg: 31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019. Vol. 2491 2019. 153783 (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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N2 - 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.

AB - 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.

M3 - Conference article in proceeding

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BT - Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg

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