English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper presents an approach for increasing the reactivity of a humanoid robot’s gait, incorporating Slow Feature Analysis (SFA), an unsupervised learning algorithm issuing from the domain of theoretical biology. The main objective of this work is to find a means to detect disturbances in the gait pattern at an early stage without losing stability. Another goal is to investigate the general potential of SFA for using it within sensorimotor loops which to our knowledge has not been considered until now. The application of SFA within sensorimotor loops is motivated by pointing out its relation to second-order Volterra filters. Our experiments show that the overall reactivity of the gait pattern increases without any profound loss in stability, and that SFA appears to be suitable for the usage even at such levels of sensorimotor control that are directly involved into motor activity regulation.

USING SLOW FEATURE ANALYSIS TO IMPROVE THE REACTIVITY OF A HUMANOID ROBOT'S SENSORIMOTOR GAIT PATTERN