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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Learning low-dimensional latent state space dynamics models has been apowerful paradigm for enabling vision-based planning and learning for control.We introduce a latent dynamics learning framework that is uniquely designed toinduce proportional controlability in the latent space, thus enabling the useof much simpler controllers than prior work. We show that our learned dynamicsmodel enables proportional control from pixels, dramatically simplifies andaccelerates behavioural cloning of vision-based controllers, and providesinterpretable goal discovery when applied to imitation learning of switchingcontrollers from demonstration.

NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces