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 study proposes a novel imitation learning approach for the stochasticgeneration of human-like rhythmic wave gestures and their modulation foreffective non-verbal communication through a probabilistic formulation usingjoint angle data from human demonstrations. This is achieved by learning andmodulating the overall expression characteristics of the gesture (e.g., armposture, waving frequency and amplitude) in the frequency domain. The methodwas evaluated on simulated robot experiments involving a robot with amanipulator of 6 degrees of freedom. The results show that the method providesefficient encoding and modulation of rhythmic movements and ensures variabilityin their execution.

Learning,Generating and Adapting Wave Gestures for Expressive Human-Robot Interaction