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Artificial Life and Robotics

, Volume 23, Issue 4, pp 489–497 | Cite as

Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation

  • Satoshi SatohEmail author
  • Kenji Fujimoto
Original Article
  • 65 Downloads

Abstract

The proposed method can generate an optimal feedforward control input and the corresponding optimal walking trajectory minimizing the \(L_2\) norm of the control input by iteration of laboratory experiments. Since a general walking motion involves discontinuous velocity transitions caused by the collision with the ground, the proposed method consists of the combination of a trajectory learning part and an estimation part of the discontinuous state transition mapping using the stored experimental data. We apply the proposed method to a kneed biped robot with a torso, where we also provide a technique to generate an optimal gait not only being energy-efficient but also avoiding the foot-scuffing problem.

Keywords

Iterative learning control Optimal control Hamiltonian systems State transition 

Notes

Acknowledgements

This work was partially supported by JSPS KAKENHI Grant Numbers JP17H03282 and JP18K04202.

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Copyright information

© ISAROB 2018

Authors and Affiliations

  1. 1.Osaka UniversitySuitaJapan
  2. 2.Kyoto UniversityKyotoJapan

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