Autonomous Motion Conference Paper 2017

Learning optimal gait parameters and impedance profiles for legged locomotion

paper
Thumb ticker sm brahayam
Movement Generation and Control

The successful execution of complex modern robotic tasks often relies on the correct tuning of a large number of parameters. In this paper we present a methodology for improving the performance of a trotting gait by learning the gait parameters, impedance profile and the gains of the control architecture. We show results on a set of terrains, for various speeds using a realistic simulation of a hydraulically actuated system. Our method achieves a reduction in the gait's mechanical energy consumption during locomotion of up to 26%. The simulation results are validated in experimental trials on the hardware system.

Author(s): Elco Heijmink and Andreea Radulescu and Brahayam Ponton and Victor Barasuol and Darwin Caldwell and Claudio Semini
Links:
Book Title: Proceedings International Conference on Humanoid Robots
Year: 2017
Month: November
Publisher: IEEE
Bibtex Type: Conference Paper (conference)
Event Name: 2017 IEEE-RAS 17th International Conference on Humanoid Robots
Event Place: Birmingham, UK
Electronic Archiving: grant_archive

BibTex

@conference{Reinforcement_Learning,
  title = {Learning optimal gait parameters and impedance profiles for legged locomotion},
  booktitle = {Proceedings International Conference on Humanoid Robots},
  abstract = {The successful execution of complex modern robotic tasks often relies on the correct tuning of a large number of parameters. In this paper we present a methodology for improving the performance of a trotting gait by learning the gait parameters, impedance profile and the gains of the control architecture. We show results on a set of terrains, for various speeds using a realistic simulation of a hydraulically actuated system. Our method achieves a reduction in the gait's mechanical energy consumption during locomotion of up to 26%. The simulation results are validated in experimental trials on the hardware system.},
  publisher = {IEEE},
  month = nov,
  year = {2017},
  slug = {reinforcement-learning},
  author = {Heijmink, Elco and Radulescu, Andreea and Ponton, Brahayam and Barasuol, Victor and Caldwell, Darwin and Semini, Claudio},
  month_numeric = {11}
}