Learning to Walk Fast: Optimized Hip Height Movement for Simulated and Real Humanoid Robots

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Shafii, N (Author) Other; lau, n (Author) FCUP; reis, lp (Author) REIT

Journal

Title: Journal of Intelligent and Robotic Systems: Theory and Applications

Vol. 80

Pages: 555-571

ISSN: 0921-0296

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00A-DCA

DOI: 10.1007/s10846-015-0191-5

Abstract (EN): The linear inverted pendulum model has been used predominantly to generate balanced humanoid walking. This model assumes that the hip height is fixed during the walk. In this paper, generating a fast walk is studied with the main focus on the effect of hip height movement. Our approach is based on modeling the hip height movement and learning its parameters in order to generate a fast walk. The hip height trajectory is generated using Fourier basis functions. The generated trajectory is the input to programmable Central Pattern Generators (CPGs) in order to modulate generated trajectories smoothly. The inverted pendulum model is utilized to model a balanced walking. A numerical approach is presented to control inverted pendulum dynamics. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to search for appropriate hip height trajectory and walking parameters that optimize walking speed. This approach has been tested not only to obtain fast forward walk but also a fast side walk. Experiments are conducted on both simulated and real NAO robots. The results show that the change from the learned forward walk to learned side walk is performed stably, which confirm the important role of using CPGs. The comparison of the results of the proposed gait model (and development approach) with those obtained using fixed hip height also shows that fixed height walking is slower than variable height walking.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17